Статті в журналах: "Debiased machine learning"

1

Ahrens, Achim, Christian B. Hansen, Mark E. Schaffer, and Thomas Wiemann. "ddml: Double/debiased machine learning in Stata." Stata Journal: Promoting communications on statistics and Stata 24, no. 1 (March 2024): 3–45. http://dx.doi.org/10.1177/1536867x241233641.

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In this article, we introduce a package, ddml, for double/debiased machine learning in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using double/debiased machine learning in combination with stacking estimation, which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.

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2

Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey. "Double/Debiased/Neyman Machine Learning of Treatment Effects." American Economic Review 107, no. 5 (May 1, 2017): 261–65. http://dx.doi.org/10.1257/aer.p20171038.

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Chernozhukov et al. (2016) provide a generic double/de-biased machine learning (ML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects and average treatment effects on the treated using observational data.

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3

Chen, Jau-er, Chien-Hsun Huang, and Jia-Jyun Tien. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions." Econometrics 9, no. 2 (April 2, 2021): 15. http://dx.doi.org/10.3390/econometrics9020015.

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In this study, we investigate the estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous study (Chernozhukov et al. 2018) and is thus relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the estimator copes well with high-dimensional controls. We also apply the procedure to empirically reinvestigate the quantile treatment effect of 401(k) participation on accumulated wealth.

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4

Chernozhukov, Victor, Whitney K. Newey, and Rahul Singh. "Automatic Debiased Machine Learning of Causal and Structural Effects." Econometrica 90, no. 3 (2022): 967–1027. http://dx.doi.org/10.3982/ecta18515.

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Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high‐dimensional, making machine learning useful. Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for linear and nonlinear functions of regressions. The debiasing is automatic in using Lasso and the function of interest without the full form of the bias correction. The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high‐dimensional methods. In addition to providing the bias correction, we give standard errors that are robust to misspecification, convergence rates for the bias correction, and primitive conditions for asymptotic inference for estimators of a variety of estimators of structural and causal effects. The automatic debiased machine learning is used to estimate the average treatment effect on the treated for the NSW job training data and to estimate demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.

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5

Liu, Molei, Yi Zhang, and Doudou Zhou. "Double/debiased machine learning for logistic partially linear model." Econometrics Journal 24, no. 3 (June 11, 2021): 559–88. http://dx.doi.org/10.1093/ectj/utab019.

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Summary We propose double/debiased machine learning approaches to infer a parametric component of a logistic partially linear model. Our framework is based on a Neyman orthogonal score equation consisting of two nuisance models for the nonparametric component of the logistic model and conditional mean of the exposure with the control group. To estimate the nuisance models, we separately consider the use of high dimensional (HD) sparse regression and (nonparametric) machine learning (ML) methods. In the HD case, we derive certain moment equations to calibrate the first order bias of the nuisance models, which preserves the model double robustness property. In the ML case, we handle the nonlinearity of the logit link through a novel and easy-to-implement ‘full model refitting’ procedure. We evaluate our methods through simulation and apply them in assessing the effect of the emergency contraceptive pill on early gestation and new births based on a 2008 policy reform in Chile.

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6

Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. "Double/debiased machine learning for treatment and structural parameters." Econometrics Journal 21, no. 1 (January 16, 2018): C1—C68. http://dx.doi.org/10.1111/ectj.12097.

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7

Chang, Neng-Chieh. "Double/debiased machine learning for difference-in-differences models." Econometrics Journal 23, no. 2 (February 4, 2020): 177–91. http://dx.doi.org/10.1093/ectj/utaa001.

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Summary This paper provides an orthogonal extension of the semiparametric difference-in-differences estimator proposed in earlier literature. The proposed estimator enjoys the so-called Neyman orthogonality (Chernozhukov et al., 2018), and thus it allows researchers to flexibly use a rich set of machine learning methods in the first-step estimation. It is particularly useful when researchers confront a high-dimensional data set in which the number of potential control variables is larger than the sample size and the conventional nonparametric estimation methods, such as kernel and sieve estimators, do not apply. I apply this orthogonal difference-in-differences estimator to evaluate the effect of tariff reduction on corruption. The empirical results show that tariff reduction decreases corruption in large magnitude.

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8

Fu, Runshan, Yan Huang, and Param Vir Singh. "Crowds, Lending, Machine, and Bias." Information Systems Research 32, no. 1 (March 1, 2021): 72–92. http://dx.doi.org/10.1287/isre.2020.0990.

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Can machines outperform crowds in financial lending decisions? Using data from a crowd-lending platform, we show that, compared with portfolios created by crowds, a reasonably sophisticated machine can construct financial portfolios that provide better returns while controlling for risk. Further, we find that the machine-created portfolios benefit not only the lenders, but also the borrowers. Borrowers receive loans at a much lower interest rate as the machine can weed out the riskiest loans better than the crowds. We also find suggestive evidence of algorithmic bias in machine decisions. We find that, compared with women, men are more likely to receive loans by machine. We propose a general and effective “debiasing” method that can be applied to any prediction-focused machine learning (ML) applications. We show that the debiased ML algorithm, which suffers from lower prediction accuracy, still improves the crowd’s investment decisions in our context. Our results indicate that ML can help crowd-lending platforms better fulfill the promise of providing access to financial resources to otherwise underserved individuals and ensure fairness in the allocation of these resources.

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9

Jung, Yonghan, Jin Tian, and Elias Bareinboim. "Estimating Identifiable Causal Effects through Double Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 12113–22. http://dx.doi.org/10.1609/aaai.v35i13.17438.

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Identifying causal effects from observational data is a pervasive challenge found throughout the empirical sciences. Very general methods have been developed to decide the identifiability of a causal quantity from a combination of observational data and causal knowledge about the underlying system. In practice, however, there are still challenges to estimating identifiable causal functionals from finite samples. Recently, a method known as double/debiased machine learning (DML) (Chernozhukov et al. 2018) has been proposed to learn parameters leveraging modern machine learning techniques, which is both robust to model misspecification and bias-reducing. Still, DML has only been used for causal estimation in settings when the back-door condition (also known as conditional ignorability) holds. In this paper, we develop a new, general class of estimators for any identifiable causal functionals that exhibit DML properties, which we name DML-ID. In particular, we introduce a complete identification algorithm that returns an influence function (IF) for any identifiable causal functional. We then construct the DML estimator based on the derived IF. We show that DML-ID estimators hold the key properties of debiasedness and doubly robustness. Simulation results corroborate with the theory.

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10

Tsai, Yun-Da, Cayon Liow, Yin Sheng Siang, and Shou-De Lin. "Toward More Generalized Malicious URL Detection Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (March 24, 2024): 21628–36. http://dx.doi.org/10.1609/aaai.v38i19.30161.

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This paper reveals a data bias issue that can profoundly hinder the performance of machine learning models in malicious URL detection. We describe how such bias can be diagnosed using interpretable machine learning techniques and further argue that such biases naturally exist in the real world security data for training a classification model. To counteract these challenges, we propose a debiased training strategy that can be applied to most deep-learning based models to alleviate the negative effects of the biased features. The solution is based on the technique of adversarial training to train deep neural networks learning invariant embedding from biased data. Through extensive experimentation, we substantiate that our innovative strategy fosters superior generalization capabilities across both CNN-based and RNN-based detection models. The findings presented in this work not only expose a latent issue in the field but also provide an actionable remedy, marking a significant step forward in the pursuit of more reliable and robust malicious URL detection.

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11

Hridi, Anurata Prabha, Rajeev Sahay, Seyyedali Hosseinalipour, and Bita Akram. "Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems." Proceedings of the AAAI Symposium Series 3, no. 1 (May 20, 2024): 297–303. http://dx.doi.org/10.1609/aaaiss.v3i1.31217.

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The majority of current research on the application of artificial intelligence (AI) and machine learning (ML) in science, technology, engineering, and mathematics (STEM) education relies on centralized model training architectures. Typically, this involves pooling data at a centralized location alongside an ML model training module, such as a cloud server. However, this approach necessitates transferring student data across the network, leading to privacy concerns. In this paper, we explore the application of federated learning (FL), a highly recognized distributed ML technique, within the educational ecosystem. We highlight the potential benefits FL offers to students, classrooms, and institutions. Also, we identify a range of technical, logistical, and ethical challenges that impede the sustainable implementation of FL in the education sector. Finally, we discuss a series of open research directions, focusing on nuanced aspects of FL implementation in educational contexts. These directions aim to explore and address the complexities of applying FL in varied educational settings, ensuring its deployment is technologically sound, beneficial, and equitable for all stakeholders involved.

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12

Jiang, Xiangxiang, Gang Lv, Minghui Li, and Kevin Lu. "CAUSAL EFFECT OF ANTI-DEMENTIA DRUGS ON PATIENTS’ ECONOMIC BURDEN USING DOUBLE/DEBIASED MACHINE LEARNING APPROACH." Innovation in Aging 7, Supplement_1 (December 1, 2023): 1000. http://dx.doi.org/10.1093/geroni/igad104.3213.

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Abstract Alzheimer’s disease and related dementia (ADRD) present a significant public health challenge and carry a substantial financial burden. Exploring avenues for treatment, the administration of anti-dementia drugs has emerged as a potential intervention for ADRD. However, prior research has been susceptible to omitted variable bias due to data limitations and struggled to establish causal relationships due to confounding by indication. Our study aims to uncover the causal impact of anti-dementia drug utilization on the economic burden among ADRD patients through Double/Debiased Machine Learning (DML) method. Leveraging data from the Medicare Current Beneficiary Survey (MCBS) spanning 2015 to 2017, we utilized a nationally representative survey linked to Medicare data, providing comprehensive insights. DML techniques, including Random Forest (RF) regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms, were employed to investigate the causal effect. After exclusion and inclusion, a total of 218,237 ADRD patients used anti-dementia drugs, with an average total medical cost of $16,695.87; 95.51% (4,646,408 patients) of ADRD patients did not use anti-dementia drug, with an average total medical cost of $27,567.09. Results from generalized linear regression showed that anti-dementia drug use did not have a significant impact on total medial cost (β = -0.29, P = 0.10). However, results from DML demonstrated a significant true effect [RF regression (β = -0.85, P = 6.79×10-3), LASSO regression (β = -0.78, P = 9.95×10-3)]. To conclude, our analysis demonstrated the causal effect of anti-dementia drug utilization in reducing overall medical costs borne by ADRD patients.

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13

Wei, Rongrong, and Yueming Xia. "Digital transformation and corporate green total factor productivity: Based on double/debiased machine learning robustness estimation." Economic Analysis and Policy 84 (December 2024): 808–27. http://dx.doi.org/10.1016/j.eap.2024.09.023.

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14

Chernozhukov, Victor, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K. Newey, and James M. Robins. "Locally Robust Semiparametric Estimation." Econometrica 90, no. 4 (2022): 1501–35. http://dx.doi.org/10.3982/ecta16294.

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Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification when there is the same number of moment functions as parameters of interest. We use these orthogonal moments and cross‐fitting to construct debiased machine learning estimators of functions of high dimensional conditional quantiles and of dynamic discrete choice parameters with high dimensional state variables. We show that additional first steps needed for the orthogonal moment functions have no effect, globally, on average orthogonal moment functions. We give a general approach to estimating those additional first steps. We characterize double robustness and give a variety of new doubly robust moment functions. We give general and simple regularity conditions for asymptotic theory.

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15

Zhang, Yingheng, Haojie Li, and Gang Ren. "Quantifying the social impacts of the London Night Tube with a double/debiased machine learning based difference-in-differences approach." Transportation Research Part A: Policy and Practice 163 (September 2022): 288–303. http://dx.doi.org/10.1016/j.tra.2022.07.015.

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16

Guo, Yaming, Meng Li, Keqiang Li, Huiping Li, and Yunxuan Li. "Unraveling the determinants of traffic incident duration: A causal investigation using the framework of causal forests with debiased machine learning." Accident Analysis & Prevention 208 (December 2024): 107806. http://dx.doi.org/10.1016/j.aap.2024.107806.

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17

Ichimura, Hidehiko, and Whitney K. Newey. "The influence function of semiparametric estimators." Quantitative Economics 13, no. 1 (2022): 29–61. http://dx.doi.org/10.3982/qe826.

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There are many economic parameters that depend on nonparametric first steps. Examples include games, dynamic discrete choice, average exact consumer surplus, and treatment effects. Often estimators of these parameters are asymptotically equivalent to a sample average of an object referred to as the influence function. The influence function is useful in local policy analysis, in evaluating local sensitivity of estimators, and constructing debiased machine learning estimators. We show that the influence function is a Gateaux derivative with respect to a smooth deviation evaluated at a point mass. This result generalizes the classic Von Mises (1947) and Hampel (1974) calculation to estimators that depend on smooth nonparametric first steps. We give explicit influence functions for first steps that satisfy exogenous or endogenous orthogonality conditions. We use these results to generalize the omitted variable bias formula for regression to policy analysis for and sensitivity to structural changes. We apply this analysis and find no sensitivity to endogeneity of average equivalent variation estimates in a gasoline demand application.

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18

Zhang, Guidong, Jianlong Wang, and Yong Liu. "How does auditing outgoing officials' natural resource asset management policies affect carbon emission efficiency? Evidence from a debiased machine learning model." Journal of Cleaner Production 478 (November 2024): 143932. http://dx.doi.org/10.1016/j.jclepro.2024.143932.

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19

包, 娣娜. "The Impact of Carbon Emission Trading on the Environmental Performance of Emission Control Enterprises—Based on Double/Debiased Machine Learning Model." Journal of Low Carbon Economy 13, no. 04 (2024): 276–84. http://dx.doi.org/10.12677/jlce.2024.134027.

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20

Zhang, Qinghua, Yuhang Chen, Yilin Zhong, and Junhao Zhong. "The positive effects of the higher education expansion policy on urban innovation in China." AIMS Mathematics 9, no. 2 (2024): 2985–3010. http://dx.doi.org/10.3934/math.2024147.

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<abstract> <p>Higher education not only enhances people's well-being, but also plays an important role in the in-depth implementation of the innovation-driven development strategy. In this paper, we use Chinese urban data for 1995–2020, utilizing the higher education expansion policy implemented in China in 1999 as an external shock. Using Double/Debiased Machine Learning (DML), we examine the impact of the aforementioned policy on urban innovation and its mechanisms. The results show that: (1) The higher education expansion policy significantly promotes urban innovation; (2) the policy promotes human capital expansion and strengthens government financial support, thereby significantly fostering urban innovation; (3) the impact of the policy varies across cities with different geographic locations, population densities and levels of marketization. Therefore, the findings of this paper provide empirical evidence that higher education expansion policy stimulates urban innovation. It also offers useful insights for China's transition from "Made in China" to "Created in China" during its high-quality development phase.</p> </abstract>

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21

Girma, Sourafel, and David Paton. "Using double-debiased machine learning to estimate the impact of Covid-19 vaccination on mortality and staff absences in elderly care homes." European Economic Review 170 (November 2024): 104882. http://dx.doi.org/10.1016/j.euroecorev.2024.104882.

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22

Nguyen-Phung, Hang Thu, and Hai Le. "Energy Poverty and Health Expenditure: Empirical Evidence from Vietnam." Social Sciences 13, no. 5 (May 6, 2024): 253. http://dx.doi.org/10.3390/socsci13050253.

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Utilizing data from the 2016 Vietnam Household Living Standard Survey, we undertake an empirical investigation into the influence of energy poverty on the health expenditure of Vietnamese households. Employing a double-hurdle model, our empirical findings reveal a negative relationship between energy poverty and health expenditure. Specifically, our results indicate that for each incremental unit increase in energy poverty, there is a substantial reduction of 42.5 percentage points in the overall health expenditure of the households. Furthermore, as energy poverty deepens, we observe declines of 24.6 percentage points and 45.5 percentage points in the expenses incurred for inpatient/outpatient care and self-treatment, respectively. To validate the robustness of our results, we conduct several sensitivity analyses, including propensity score matching, double/debiased machine learning. Across all these methods, our findings consistently underscore the significant and persistent adverse impact of energy poverty on the examined outcome variables. Additionally, to examine the underlying pathways, we conduct a structural equation modeling analysis and find that the relationship between energy poverty and health expenses is mediated by household hospitalization and expenditures on essential items, such as food and daily necessities.

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23

Olivares, Barlin O., Juan C. Rey, Guillermo Perichi, and Deyanira Lobo. "Relationship of Microbial Activity with Soil Properties in Banana Plantations in Venezuela." Sustainability 14, no. 20 (October 19, 2022): 13531. http://dx.doi.org/10.3390/su142013531.

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The present work aims to analyze the relationship of microbial activity with the physicochemical properties of the soil in banana plantations in Venezuela. Six agricultural fields located in two of the main banana production areas of Venezuela were selected. The experimental sites were differentiated with two levels of productivity (high and low) of the “Gran Nain” banana. Ten variables were selected: total free-living nematodes (FLN), bacteriophages, predators, omnivores, Phytonematodes, saturated hydraulic conductivity, total organic carbon, nitrate (NO3), microbial respiration and the variable other fungi. Subsequently, machine learning algorithms were used. First, the Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to find the soil properties that could distinguish the banana productivity levels. Second, the Debiased Sparse Partial Correlation (DSPC) algorithm was applied to obtain the correlation network of the most important variables. The variable free-living nematode predators had a degree of 3 and a betweenness of 4 in the correlation network, followed by NO3. The network shows positive correlations between FLN predators and microbial respiration (r = 1.00; p = 0.014), and NO3 (r = 1.00; p = 0.032). The selected variables are proposed to characterize the soil productivity in bananas and could be used for the management of soil diseases affecting bananas.

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24

Li, Hairui, Xuemei Liu, Xiaolu Chen, and Xianfeng Huai. "Robust Anomaly Recognition in Hydraulic Structural Safety Monitoring: A Methodology Based on Deconfounding Boosted Regression Trees." Mathematical Problems in Engineering 2023 (August 16, 2023): 1–15. http://dx.doi.org/10.1155/2023/7854792.

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Analyzing monitoring data to recognize structural anomalies is a typical intelligent application of structural safety monitoring, which is of great significance to hydraulic engineering operational management. Many regression modeling methods have been developed to describe the complex statistical relationships between engineering safety monitoring points, which in turn can be used to recognize abnormal data. However, existing studies are devoted to introducing the correlation between adjacent response points to improve prediction accuracy, ignoring the detrimental effects on anomaly recognition, especially the pseudo-regression problem. In this paper, an anomaly recognition method is proposed from the perspective of causal inference to realize the best exploitation of various types of monitoring information in model construction, including four steps of constructing causal graph, regression modeling, model interpretation, and anomaly recognition. In regression modeling stage, two deconfounding machine learning models, two-stage boosted regression trees and copula debiased boosted regression trees, are proposed for recovering the causal effects of correlated response points. The validation was carried out with Shanmen River culvert monitoring data, and experiment results showed that the proposed method in this paper has better anomaly recognition compared to existing regression modeling methods, as shown by lower false alarm rates and lower averaged missing alarm rates under different structural anomaly scenarios.

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Li, Enji, Qing Chen, Xinyan Zhang, and Chen Zhang. "Digital Government Development, Local Governments’ Attention Distribution and Enterprise Total Factor Productivity: Evidence from China." Sustainability 15, no. 3 (January 30, 2023): 2472. http://dx.doi.org/10.3390/su15032472.

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Building digital government is an important means for the government to improve the public service ability and optimize the business environment, which directly affects the production and operation activities of micro-enterprises. Based on the panel data of listed enterprises and municipal government portal website performance in China, this paper empirically investigates the impact of digital government development on enterprise total factor productivity (TFP) and the moderating effect of the local government’s attention distribution. The research results showed that digital government development significantly improved the enterprise TFP, and this conclusion remained unchanged after a series of robustness tests using instrumental variables, one-stage lag of explained variables, and debiased machine learning models. We also found that the greater the pressure faced by local governments and the longer the chief officials’ tenure, the more attention local governments paid to building digital government, and the more obvious the role of digital government development in promoting enterprise TFP. Heterogeneity test results showed that the information disclosure, online service, and public participation all had a positive effect on enterprise TFP, while the user experience had no effect on it. Digital government development had a more obvious role in promoting enterprise TFP of central and western regions, non-SOEs, and technology-intensive enterprises. Moreover, reducing enterprise rent-seeking, attracting new enterprise entry, and increasing enterprise R&D investment are important mechanisms for digital government development to improve enterprise TFP.

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Pinkava, Thomas, Jack McFarland, and Afra Mashhadi. "A Model- and Data-Agnostic Debiasing System for Achieving Equalized Odds." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 1123–31. http://dx.doi.org/10.1609/aies.v7i1.31709.

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As reliance on Machine Learning (ML) systems in real-world decision-making processes grows, ensuring these systems are free of bias against sensitive demographic groups is of increasing importance. Existing techniques for automatically debiasing ML models generally require access to either the models’ internal architectures, the models’ training datasets, or both. In this paper we outline the reasons why such requirements are disadvantageous, and present an alternative novel debiasing system that is both data- and model-agnostic. We implement this system as a Reinforcement Learning Agent and through extensive experiments show that we can debias a variety of target ML model architectures over three benchmark datasets. Our results show performance comparable to data- and/or model-gnostic state-of-the-art debiasers.

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Chowdhury, Somnath Basu Roy, and Snigdha Chaturvedi. "Learning Fair Representations via Rate-Distortion Maximization." Transactions of the Association for Computational Linguistics 10 (2022): 1159–74. http://dx.doi.org/10.1162/tacl_a_00512.

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Abstract Text representations learned by machine learning models often encode undesirable demographic information of the user. Predictive models based on these representations can rely on such information, resulting in biased decisions. We present a novel debiasing technique, Fairness-aware Rate Maximization (FaRM), that removes protected information by making representations of instances belonging to the same protected attribute class uncorrelated, using the rate-distortion function. FaRM is able to debias representations with or without a target task at hand. FaRM can also be adapted to remove information about multiple protected attributes simultaneously. Empirical evaluations show that FaRM achieves state-of-the-art performance on several datasets, and learned representations leak significantly less protected attribute information against an attack by a non-linear probing network.

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Islam, Rashidul, Huiyuan Chen, and Yiwei Cai. "Fairness without Demographics through Shared Latent Space-Based Debiasing." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12717–25. http://dx.doi.org/10.1609/aaai.v38i11.29167.

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Ensuring fairness in machine learning (ML) is crucial, particularly in applications that impact diverse populations. The majority of existing works heavily rely on the availability of protected features like race and gender. However, practical challenges such as privacy concerns and regulatory restrictions often prohibit the use of this data, limiting the scope of traditional fairness research. To address this, we introduce a Shared Latent Space-based Debiasing (SLSD) method that transforms data from both the target domain, which lacks protected features, and a separate source domain, which contains these features, into correlated latent representations. This allows for joint training of a cross-domain protected group estimator on the representations. We then debias the downstream ML model with an adversarial learning technique that leverages the group estimator. We also present a relaxed variant of SLSD, the R-SLSD, that occasionally accesses a small subset of protected features from the target domain during its training phase. Our extensive experiments on benchmark datasets demonstrate that our methods consistently outperform existing state-of-the-art models in standard group fairness metrics.

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Rana, Saadia Afzal, Zati Hakim Azizul, and Ali Afzal Awan. "A step toward building a unified framework for managing AI bias." PeerJ Computer Science 9 (October 26, 2023): e1630. http://dx.doi.org/10.7717/peerj-cs.1630.

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Integrating artificial intelligence (AI) has transformed living standards. However, AI’s efforts are being thwarted by concerns about the rise of biases and unfairness. The problem advocates strongly for a strategy for tackling potential biases. This article thoroughly evaluates existing knowledge to enhance fairness management, which will serve as a foundation for creating a unified framework to address any bias and its subsequent mitigation method throughout the AI development pipeline. We map the software development life cycle (SDLC), machine learning life cycle (MLLC) and cross industry standard process for data mining (CRISP-DM) together to have a general understanding of how phases in these development processes are related to each other. The map should benefit researchers from multiple technical backgrounds. Biases are categorised into three distinct classes; pre-existing, technical and emergent bias, and subsequently, three mitigation strategies; conceptual, empirical and technical, along with fairness management approaches; fairness sampling, learning and certification. The recommended practices for debias and overcoming challenges encountered further set directions for successfully establishing a unified framework.

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30

Maasoumi, Esfandiar Essie, Jianqiu Wang, Zhuo Wang, and Ke Wu. "Identifying Factors via Automatic Debiased Machine Learning." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4223091.

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31

Chiang, Harold D., Kengo Kato, Yukun Ma, and Yuya Sasaki. "Multiway Cluster Robust Double/Debiased Machine Learning." Journal of Business & Economic Statistics, April 19, 2021, 1–11. http://dx.doi.org/10.1080/07350015.2021.1895815.

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32

Ahrens, Achim, Christian Hansen, Mark Schaffer, and Thomas T. Wiemann. "Ddml: Double/Debiased Machine Learning in Stata." SSRN Electronic Journal, 2023. http://dx.doi.org/10.2139/ssrn.4368837.

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33

Maasoumi, Esfandiar, Jianqiu Wang, Zhuo Wang, and Ke Wu. "Identifying factors via automatic debiased machine learning." Journal of Applied Econometrics, February 13, 2024. http://dx.doi.org/10.1002/jae.3031.

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SummaryIdentifying risk factors that have significant explanatory power for the cross‐sectional asset returns is fundamental in asset pricing. We adopt a novel automatic debiased machine learning (ADML) method proposed by Chernozhukov, Newey, and Singh (2022) to robustly estimate partial pricing effect of a certain factor controlling for a large number of confounding factors under a nonlinear stochastic discount factor (SDF) assumption. The ADML resolves biased estimation, non‐robustness, and overfitting issues that are common to traditional machine learning approaches. We find that the most significant factors selected by the ADML outperform the Fama–French sparse factors and factors identified via the double‐selection LASSO method under a linear factor model assumption. Out of a high‐dimensional zoo of US stock market factors commonly tested in the finance literature, we identify approximately 30 to 50 factors having significant but declining pricing power in explaining the cross‐section of stock returns. Our findings are robust to hyperparameter settings and choices of test assets and machine learning methods.

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34

Semenova, Vira. "Debiased machine learning of set-identified linear models." Journal of Econometrics, January 2023. http://dx.doi.org/10.1016/j.jeconom.2022.12.010.

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35

Chernozhukov, V., W. K. Newey, and R. Singh. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees." Biometrika, June 14, 2022. http://dx.doi.org/10.1093/biomet/asac033.

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Summary Debiased machine learning is a meta algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals, i.e., scalar summaries, of machine learning algorithms. For example, an analyst may desire the confidence interval for a treatment effect estimated with a neural network. We provide a non-asymptotic debiased machine learning theorem that encompasses any global or local functional of any machine learning algorithm that satisfies a few simple, interpretable conditions. Formally, we prove consistency, Gaussian approximation, and semiparametric efficiency by finite sample arguments. The rate of convergence is n−1/2 for global functionals, and it degrades gracefully for local functionals. Our results culminate in a simple set of conditions that an analyst can use to translate modern learning theory rates into traditional statistical inference. The conditions reveal a general double robustness property for ill-posed inverse problems.

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36

Dai, Xiaowu, and Lexin Li. "Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis." Journal of the American Statistical Association, February 3, 2022, 1–15. http://dx.doi.org/10.1080/01621459.2021.2013851.

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37

Kim, Gene Hyungjin. "Double/Debiased Machine Learning for Static Games with Incomplete Information." SSRN Electronic Journal, 2023. http://dx.doi.org/10.2139/ssrn.4377695.

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38

Semenova, Vira, and Victor Chernozhukov. "Debiased machine learning of conditional average treatment effects and other causal functions." Econometrics Journal, August 29, 2020. http://dx.doi.org/10.1093/ectj/utaa027.

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Summary This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning tools. We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate by modern machine learning techniques. We first adjust the signal to make it insensitive (Neyman-orthogonal) with respect to the first-stage regularisation bias. We then project the signal onto a set of basis functions, which grow with sample size, to get the best linear predictor of the structural function. We derive a complete set of results for estimation and simultaneous inference on all parameters of the best linear predictor, conducting inference by Gaussian bootstrap. When the structural function is smooth and the basis is sufficiently rich, our estimation and inference results automatically target this function. When basis functions are group indicators, the best linear predictor reduces to the group average treatment/structural effect, and our inference automatically targets these parameters. We demonstrate our method by estimating uniform confidence bands for the average price elasticity of gasoline demand conditional on income.

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39

Jin, Zequn, Lihua Lin, and Zhengyu Zhang. "Identification and Auto-debiased Machine Learning for Outcome-Conditioned Average Structural Derivatives." Journal of Business & Economic Statistics, January 24, 2024, 1–27. http://dx.doi.org/10.1080/07350015.2024.2310022.

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40

Shi, Bowen, Xiaojie Mao, Mochen Yang, and Bo Li. "What, Why, and How: An Empiricist's Guide to Double/Debiased Machine Learning." SSRN Electronic Journal, 2024. http://dx.doi.org/10.2139/ssrn.4677153.

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41

Díaz, Iván. "Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning." Biostatistics, November 19, 2019. http://dx.doi.org/10.1093/biostatistics/kxz042.

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Summary In recent decades, the fields of statistical and machine learning have seen a revolution in the development of data-adaptive regression methods that have optimal performance under flexible, sometimes minimal, assumptions on the true regression functions. These developments have impacted all areas of applied and theoretical statistics and have allowed data analysts to avoid the biases incurred under the pervasive practice of parametric model misspecification. In this commentary, I discuss issues around the use of data-adaptive regression in estimation of causal inference parameters. To ground ideas, I focus on two estimation approaches with roots in semi-parametric estimation theory: targeted minimum loss-based estimation (TMLE; van der Laan and Rubin, 2006) and double/debiased machine learning (DML; Chernozhukov and others, 2018). This commentary is not comprehensive, the literature on these topics is rich, and there are many subtleties and developments which I do not address. These two frameworks represent only a small fraction of an increasingly large number of methods for causal inference using machine learning. To my knowledge, they are the only methods grounded in statistical semi-parametric theory that also allow unrestricted use of data-adaptive regression techniques.

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42

Moccia, Chiara, Giovenale Moirano, Maja Popovic, Costanza Pizzi, Piero Fariselli, Lorenzo Richiardi, Claus Thorn Ekstrøm, and Milena Maule. "Machine learning in causal inference for epidemiology." European Journal of Epidemiology, November 13, 2024. http://dx.doi.org/10.1007/s10654-024-01173-x.

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AbstractIn causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimates. Correct model specification is challenging, especially in high-dimensional settings. Incorporating Machine Learning (ML) into causal analyses may reduce the bias arising from model misspecification, since ML methods do not require the specification of a functional form of the relationship between variables. However, when ML predictions are directly plugged in a predefined formula of the effect of interest, there is the risk of introducing a “plug-in bias” in the effect measure. To overcome this problem and to achieve useful asymptotic properties, new estimators that combine the predictive potential of ML and the ability of traditional statistical methods to make inference about population parameters have been proposed. For epidemiologists interested in taking advantage of ML for causal inference investigations, we provide an overview of three estimators that represent the current state-of-art, namely Targeted Maximum Likelihood Estimation (TMLE), Augmented Inverse Probability Weighting (AIPW) and Double/Debiased Machine Learning (DML).

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43

Feyzollahi, Maryam, and Nima Rafizadeh. "Double/Debiased Machine Learning for Economists: Practical Guidelines, Best Practices, and Common Pitfalls." SSRN Electronic Journal, 2024. http://dx.doi.org/10.2139/ssrn.4703243.

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44

Bhagavathula, Akshaya Srikanth. "Causal Analysis of PFAS Exposure and Cardiovascular Risk Using Double/Debiased Machine Learning." Annals of Epidemiology, August 2024. http://dx.doi.org/10.1016/j.annepidem.2024.07.050.

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45

Kabata, Daijiro, and Mototsugu Shintani. "Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach." Communications in Statistics - Simulation and Computation, November 15, 2021, 1–14. http://dx.doi.org/10.1080/03610918.2021.2001655.

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46

Loecher, Markus. "Debiasing SHAP scores in random forests." AStA Advances in Statistical Analysis, August 22, 2023. http://dx.doi.org/10.1007/s10182-023-00479-7.

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AbstractBlack box machine learning models are currently being used for high-stakes decision making in various parts of society such as healthcare and criminal justice. While tree-based ensemble methods such as random forests typically outperform deep learning models on tabular data sets, their built-in variable importance algorithms are known to be strongly biased toward high-entropy features. It was recently shown that the increasingly popular SHAP (SHapley Additive exPlanations) values suffer from a similar bias. We propose debiased or "shrunk" SHAP scores based on sample splitting which additionally enable the detection of overfitting issues at the feature level.

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47

Wang, Clarice, Kathryn Wang, Andrew Y. Bian, Rashidul Islam, Kamrun Naher Keya, James Foulds, and Shimei Pan. "When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation." ACM Transactions on Interactive Intelligent Systems, August 2023. http://dx.doi.org/10.1145/3611313.

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Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g. along lines of gender, age, and race. While most research in this domain focuses on developing fair AI algorithms, in this work, we examine the challenges which arise when humans and fair AI interact. Our results show that due to an apparent conflict between human preferences and fairness, a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using college major recommendation as a case study, we build a fair AI recommender by employing gender debiasing machine learning techniques. Our offline evaluation showed that the debiased recommender makes fairer career recommendations without sacrificing its accuracy in prediction. Nevertheless, an online user study of more than 200 college students revealed that participants on average prefer the original biased system over the debiased system. Specifically, we found that perceived gender disparity is a determining factor for the acceptance of a recommendation. In other words, we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans. We conducted a follow-up survey to gain additional insights into the effectiveness of various design options that can help participants to overcome their own biases. Our results suggest that making fair AI explainable is crucial for increasing its adoption in the real world.

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48

Zhong, Junhao, Zhenzhen Wang, and Yunfeng Deng. "Driving the Green Transformation of Enterprises: The Role of Patent Insurance." Managerial and Decision Economics, November 21, 2024. http://dx.doi.org/10.1002/mde.4439.

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ABSTRACTWe verified the effect of patent insurance policy implementation on corporate green transformation, using a dataset of Chinese‐listed companies from 2007 to 2019. Using double/debiased machine learning and text analysis, we found that the pilot policy of patent insurance has a significant positive impact on the green transformation of enterprises. This effect is significant in heavily polluting industries, eastern regions, state‐owned enterprises, and companies in a growth stage. The patent insurance pilot policy promotes corporate green transformation by alleviating financing constraints, reducing operational risks, and increasing green output.

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Kamal, Kimia, and Bilal Farooq. "Debiased Machine Learning for Estimating the Causal Effect of Urban Traffic on Pedestrian Crossing Behavior." Transportation Research Record: Journal of the Transportation Research Board, February 8, 2023, 036119812311522. http://dx.doi.org/10.1177/03611981231152246.

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Before the transition of automated vehicles (AVs) to urban roads and subsequently unprecedented changes in traffic conditions, the evaluation of transportation policies and futuristic road design related to pedestrian crossing behavior is of vital importance. Recent studies analyzed the non-causal impact of various variables on pedestrian waiting time in the presence of AVs. However, we mainly investigate the causal effect of traffic density on pedestrian waiting time. We develop a double/debiased machine learning (DML) model in which the impact of the confounders variable influencing both a policy and an outcome of interest is addressed, resulting in unbiased policy evaluation. Furthermore, we try to analyze the effect of traffic density by developing a copula-based joint model of the two main components of pedestrian crossing behavior, pedestrian stress level and waiting time. The copula approach has been widely used in the literature for addressing self-selection problems, which can be classified as a causality analysis in travel behavior modeling. The results obtained from copula approach and DML are compared based on the effect of traffic density. In the DML model structure, the standard error term of the density parameter is lower than that of the copula approach and the confidence interval is considerably more reliable. In addition, despite the similar sign of effect, the copula approach estimates the effect of traffic density lower than DML, because of the spurious effect of the confounders. In short, the DML model structure can flexibly adjust the impact of confounders by using machine learning algorithms and is more reliable for planning future policies.

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Kovács, Dávid Péter, William McCorkindale, and Alpha A. Lee. "Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias." Nature Communications 12, no. 1 (March 16, 2021). http://dx.doi.org/10.1038/s41467-021-21895-w.

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AbstractOrganic synthesis remains a major challenge in drug discovery. Although a plethora of machine learning models have been proposed as solutions in the literature, they suffer from being opaque black-boxes. It is neither clear if the models are making correct predictions because they inferred the salient chemistry, nor is it clear which training data they are relying on to reach a prediction. This opaqueness hinders both model developers and users. In this paper, we quantitatively interpret the Molecular Transformer, the state-of-the-art model for reaction prediction. We develop a framework to attribute predicted reaction outcomes both to specific parts of reactants, and to reactions in the training set. Furthermore, we demonstrate how to retrieve evidence for predicted reaction outcomes, and understand counterintuitive predictions by scrutinising the data. Additionally, we identify Clever Hans predictions where the correct prediction is reached for the wrong reason due to dataset bias. We present a new debiased dataset that provides a more realistic assessment of model performance, which we propose as the new standard benchmark for comparing reaction prediction models.

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