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Journal articles on the topic 'Causal machine learning'

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Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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1

Weiser, Michael, Stefan Feuerriegel, and Tim Herrmann. "Causal Machine Learning." Controlling 32, no. 3 (2020): 86–87. http://dx.doi.org/10.15358/0935-0381-2020-3-86.

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2

Walker, Caren M., Alexandra Rett, and Elizabeth Bonawitz. "Design Drives Discovery in Causal Learning." Psychological Science 31, no. 2 (January 21, 2020): 129–38. http://dx.doi.org/10.1177/0956797619898134.

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We assessed whether an artifact’s design can facilitate recognition of abstract causal rules. In Experiment 1, 152 three-year-olds were presented with evidence consistent with a relational rule (i.e., pairs of same or different blocks activated a machine) using two differently designed machines. In the standard-design condition, blocks were placed on top of the machine; in the relational-design condition, blocks were placed into openings on either side. In Experiment 2, we assessed whether this design cue could facilitate adults’ ( N = 102) inference of a distinct conjunctive cause (i.e., that two blocks together activate the machine). Results of both experiments demonstrated that causal inference is sensitive to an artifact’s design: Participants in the relational-design conditions were more likely to infer rules that were a priori unlikely. Our findings suggest that reasoning failures may result from difficulty generating the relevant rules as cognitive hypotheses but that artifact design aids causal inference. These findings have clear implications for creating intuitive learning environments.
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Zhao, Yang, and Qing Liu. "Causal ML: Python package for causal inference machine learning." SoftwareX 21 (February 2023): 101294. http://dx.doi.org/10.1016/j.softx.2022.101294.

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4

Goodman, Steven N., Sharad Goel, and Mark R. Cullen. "Machine Learning, Health Disparities, and Causal Reasoning." Annals of Internal Medicine 169, no. 12 (December 4, 2018): 883. http://dx.doi.org/10.7326/m18-3297.

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5

Huenermund, Paul, Jermain Christopher Kaminski, and Carla Schmitt. "Causal Machine Learning and Business Decision Making." Academy of Management Proceedings 2021, no. 1 (August 2021): 12517. http://dx.doi.org/10.5465/ambpp.2021.12517abstract.

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6

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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Arti, Shindy, Indriana Hidayah, and Sri Suning Kusumawardhani. "Research Trend of Causal Machine Learning Method: A Literature Review." IJID (International Journal on Informatics for Development) 9, no. 2 (December 31, 2020): 111–18. http://dx.doi.org/10.14421/ijid.2020.09208.

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Machine learning is commonly used to predict and implement pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.
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8

Sasou, Akira. "Deep Residual Learning With Dilated Causal Convolution Extreme Learning Machine." IEEE Access 9 (2021): 165708–18. http://dx.doi.org/10.1109/access.2021.3134700.

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9

Zhao, Yiqing, Yue Yu, Hanyin Wang, Yikuan Li, Yu Deng, Guoqian Jiang, and Yuan Luo. "Machine Learning in Causal Inference: Application in Pharmacovigilance." Drug Safety 45, no. 5 (May 2022): 459–76. http://dx.doi.org/10.1007/s40264-022-01155-6.

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10

Crown, William H. "Real-World Evidence, Causal Inference, and Machine Learning." Value in Health 22, no. 5 (May 2019): 587–92. http://dx.doi.org/10.1016/j.jval.2019.03.001.

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11

Marafino, Ben J., Alejandro Schuler, Vincent X. Liu, Gabriel J. Escobar, and Mike Baiocchi. "Predicting preventable hospital readmissions with causal machine learning." Health Services Research 55, no. 6 (October 30, 2020): 993–1002. http://dx.doi.org/10.1111/1475-6773.13586.

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12

Wu, Peng, Qi-rui Hu, Xing-wei Tong, and Min Wu. "Learning Causal Effect Using Machine Learning with Application to China’s Typhoon." Acta Mathematicae Applicatae Sinica, English Series 36, no. 3 (July 2020): 702–13. http://dx.doi.org/10.1007/s10255-020-0960-1.

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13

Varian, Hal R. "Causal inference in economics and marketing." Proceedings of the National Academy of Sciences 113, no. 27 (July 5, 2016): 7310–15. http://dx.doi.org/10.1073/pnas.1510479113.

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This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.
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Molina, Mario, and Filiz Garip. "Machine Learning for Sociology." Annual Review of Sociology 45, no. 1 (July 30, 2019): 27–45. http://dx.doi.org/10.1146/annurev-soc-073117-041106.

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Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and sociology. We offer a brief introduction to this vast toolbox and illustrate its current uses in the social sciences, including distilling measures from new data sources, such as text and images; characterizing population heterogeneity; improving causal inference; and offering predictions to aid policy decisions and theory development. We argue that, in addition to serving similar purposes in sociology, machine learning tools can speak to long-standing questions on the limitations of the linear modeling framework, the criteria for evaluating empirical findings, transparency around the context of discovery, and the epistemological core of the discipline.
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15

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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Rathje, Jason Michael, Riitta Katila, and Philipp Reineke. "Causal Inference in Strategy: How can Machine Learning Help?" Academy of Management Proceedings 2021, no. 1 (August 2021): 14156. http://dx.doi.org/10.5465/ambpp.2021.14156abstract.

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17

Moraffah, Raha, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, and Huan Liu. "Causal Interpretability for Machine Learning - Problems, Methods and Evaluation." ACM SIGKDD Explorations Newsletter 22, no. 1 (May 13, 2020): 18–33. http://dx.doi.org/10.1145/3400051.3400058.

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18

Lin, Sheng-Hsuan, and Mohammad Arfan Ikram. "On the relationship of machine learning with causal inference." European Journal of Epidemiology 35, no. 2 (September 27, 2019): 183–85. http://dx.doi.org/10.1007/s10654-019-00564-9.

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19

Cui, Peng, and Susan Athey. "Stable learning establishes some common ground between causal inference and machine learning." Nature Machine Intelligence 4, no. 2 (February 2022): 110–15. http://dx.doi.org/10.1038/s42256-022-00445-z.

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20

Wasserbacher, Helmut, and Martin Spindler. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls." Digital Finance 4, no. 1 (December 16, 2021): 63–88. http://dx.doi.org/10.1007/s42521-021-00046-2.

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AbstractThis article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A). Machine learning appears well suited to support FP&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.
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21

Brand, Jennie E., Jiahui Xu, Bernard Koch, and Pablo Geraldo. "Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning." Sociological Methodology 51, no. 2 (March 4, 2021): 189–223. http://dx.doi.org/10.1177/0081175021993503.

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Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score–based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.
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22

Fei, Nina, Youlong Yang, and Xuying Bai. "One Core Task of Interpretability in Machine Learning — Expansion of Structural Equation Modeling." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 01 (June 6, 2019): 2051001. http://dx.doi.org/10.1142/s0218001420510015.

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Structural equation modeling (SEM) is a system of two kinds of equations: a linear latent structural model (SM) and a linear measurement model (MM). The latent structure model is a causal model from the latent parent node to the latent child node. Meanwhile, MM’s link is from latent variable parent node to observed variable child node. However, researchers should determine the initial causal order between variables based on experience when applying SEM. The main reason is that SEM does not fully construct causal models between observed variables (OVs) from big data. When the artificial causal order is contrary to the fact, the causal inference from SEM is doubtful, and the implicit causal information between the OVs cannot be extracted and utilized. This study first objectively identifies the causal order of variables using the DirectLiNGAM method widely accepted in recent years. Then traditional SEM is converted to expanded SEM (ESEM) consisting of SM, MM and observation model (OM). Finally, through model testing and debugging, ESEM with good fit with data is obtained.
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23

Marafino, Ben J., Alejandro Schuler, Vincent X. Liu, Gabriel J. Escobar, and Mike Baiocchi. "Correction to “Predicting preventable hospital readmissions with causal machine learning”." Health Services Research 56, no. 1 (January 27, 2021): 168. http://dx.doi.org/10.1111/1475-6773.13611.

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24

Kim, Yeji, Taehwa Choi, and Sangbum Choi. "Exploring modern machine learning methods to improve causal-effect estimation." Communications for Statistical Applications and Methods 29, no. 2 (March 31, 2022): 177–91. http://dx.doi.org/10.29220/csam.2022.29.2.177.

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25

Makarov, Ilya, Andrey Savchenko, Arseny Korovko, Leonid Sherstyuk, Nikita Severin, Dmitrii Kiselev, Aleksandr Mikheev, and Dmitrii Babaev. "Temporal network embedding framework with causal anonymous walks representations." PeerJ Computer Science 8 (January 20, 2022): e858. http://dx.doi.org/10.7717/peerj-cs.858.

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Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic (i.e., temporal) network is analyzed. In this paper, we propose a novel approach for dynamic network representation learning based on Temporal Graph Network by using a highly custom message generating function by extracting Causal Anonymous Walks. We provide a benchmark pipeline for the evaluation of temporal network embeddings. This work provides the first comprehensive comparison framework for temporal network representation learning for graph machine learning problems involving node classification and link prediction in every available setting. The proposed model outperforms state-of-the-art baseline models. The work also justifies their difference based on evaluation in various transductive/inductive edge/node classification tasks. In addition, we show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks, involving credit scoring based on transaction data.
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26

Lin, Fan, Elena Z. Lazarus, and Seung Y. Rhee. "QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants." G3: Genes|Genomes|Genetics 10, no. 7 (May 19, 2020): 2411–21. http://dx.doi.org/10.1534/g3.120.401122.

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Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.
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27

Zeng, Jiaming. "Developing a Machine Learning Tool for Dynamic Cancer Treatment Strategies." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13742–43. http://dx.doi.org/10.1609/aaai.v34i10.7143.

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With the rising number and complexity of cancer therapies, it is increasingly difficult for clinicians to identity an optimal combination of treatments for a patient. Our research aims to provide a decision support tool to optimize and supplant cancer treatment decisions. Leveraging machine learning, causal inference, and decision analysis, we will utilize electronic medical records to develop dynamic cancer treatment strategies that advice clinicians and patients based on patient characteristics, medical history, and etc. The research hopes to bridge the understanding between causal inference and decision analysis and ultimately develops an artificial intelligence tool that improves clinical outcomes over current practices.
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28

Storm, Hugo, Kathy Baylis, and Thomas Heckelei. "Machine learning in agricultural and applied economics." European Review of Agricultural Economics 47, no. 3 (August 21, 2019): 849–92. http://dx.doi.org/10.1093/erae/jbz033.

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AbstractThis review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.
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29

Leist, Anja K. "SOCIAL AND BEHAVIORAL FACTORS IN COGNITIVE AGING: APPLYING THE CAUSAL INFERENCE FRAMEWORK IN OBSERVATIONAL STUDIES." Innovation in Aging 3, Supplement_1 (November 2019): S323. http://dx.doi.org/10.1093/geroni/igz038.1178.

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Abstract Rationale: There is an urgent need to better understand how to maintain cognitive functioning at older ages with social and behavioral interventions, given that there is currently no medical cure available to prevent, halt or reverse the progression of cognitive decline and dementia. However, in current models, it is still not well established which factors (e.g. education, BMI, physical activity, sleep, depression) matter most at which ages, and which behavioral profiles are most protective against cognitive decline. In the last years, advances in the fields of causal inference and machine learning have equipped epidemiology and social sciences with methods and models to approach causal questions in observational studies. Method: The presentation will give an overview of the causal inference framework and different machine learning approaches to investigate cognitive aging. First, we will present relevant research questions on the role of social and behavioral factors in cognitive aging in observational studies. Second, we will introduce the causal inference framework and recent methods to visualize and compute the strength of causal paths. Third, promising machine learning approaches to arrive at robust predictions are presented. The 13-year follow-up from the European SHARE survey that employs well-established cognitive performance tests is used to demonstrate the usefulness of the approach. Discussion: The causal inference framework, combined with recent machine learning approaches and applied in observational studies, provides a robust alternative to intervention research. Advantages for investigations under the new framework, e.g., fewer ethical considerations compared to intervention research, as well as limitations are discussed.
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30

Su, Cong, Guoxian Yu, Jun Wang, Zhongmin Yan, and Lizhen Cui. "A review of causality-based fairness machine learning." Intelligence & Robotics 2, no. 3 (2022): 244–74. http://dx.doi.org/10.20517/ir.2022.17.

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With the wide application of machine learning driven automated decisions (e.g., education, loan approval, and hiring) in daily life, it is critical to address the problem of discriminatory behavior toward certain individuals or groups. Early studies focused on defining the correlation/association-based notions, such as statistical parity, equalized odds, etc. However, recent studies reflect that it is necessary to use causality to address the problem of fairness. This review provides an exhaustive overview of notions and methods for detecting and eliminating algorithmic discrimination from a causality perspective. The review begins by introducing the common causality-based definitions and measures for fairness. We then review causality-based fairness-enhancing methods from the perspective of pre-processing, in-processing and post-processing mechanisms, and conduct a comprehensive analysis of the advantages, disadvantages, and applicability of these mechanisms. In addition, this review also examines other domains where researchers have observed unfair outcomes and the ways they have tried to address them. There are still many challenges that hinder the practical application of causality-based fairness notions, specifically the difficulty of acquiring causal graphs and identifiability of causal effects. One of the main purposes of this review is to spark more researchers to tackle these challenges in the near future.
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31

Yao, Liuyi, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. "A Survey on Causal Inference." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (June 26, 2021): 1–46. http://dx.doi.org/10.1145/3444944.

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Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.
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32

Ludwig, Jens, Sendhil Mullainathan, and Jann Spiess. "Augmenting Pre-Analysis Plans with Machine Learning." AEA Papers and Proceedings 109 (May 1, 2019): 71–76. http://dx.doi.org/10.1257/pandp.20191070.

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Concerns about the dissemination of spurious results have led to calls for pre-analysis plans (PAPs) to avoid ex-post “p-hacking.” But often the conceptual hypotheses being tested do not imply the level of specificity required for a PAP. In this paper we suggest a framework for PAPs that capitalize on the availability of causal machine-learning (ML) techniques, in which researchers combine specific aspects of the analysis with ML for the flexible estimation of unspecific remainders. A “cheap-lunch” result shows that the inclusion of ML produces limited worst-case costs in power, while offering a substantial upside from systematic specification searches.
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33

Zeng, Zengri, Wei Peng, and Baokang Zhao. "Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning." Security and Communication Networks 2021 (November 3, 2021): 1–18. http://dx.doi.org/10.1155/2021/8986243.

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In recent years, machine learning (ML) algorithms have been approved effective in the intrusion detection. However, as the ML algorithms are mainly applied to evaluate the anomaly of the network, the detection accuracy for cyberattacks with multiple types cannot be fully guaranteed. The existing algorithms for network intrusion detection based on ML or feature selection are on the basis of spurious correlation between features and cyberattacks, causing several wrong classifications. In order to tackle the abovementioned problems, this research aimed to establish a novel network intrusion detection system (NIDS) based on causal ML. The proposed system started with the identification of noisy features by causal intervention, while only the features that had a causality with cyberattacks were preserved. Then, the ML algorithm was used to make a preliminary classification to select the most relevant types of cyberattacks. As a result, the unique labeled cyberattack could be detected by the counterfactual detection algorithm. In addition to a relatively stable accuracy, the complexity of cyberattack detection could also be effectively reduced, with a maximum reduction to 94% on the size of training features. Moreover, in case of the availability of several types of cyberattacks, the detection accuracy was significantly improved compared with the previous ML algorithms.
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Hejazi, Nima, Kara Rudolph, and Iván Díaz. "medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R." Journal of Open Source Software 7, no. 69 (January 5, 2022): 3979. http://dx.doi.org/10.21105/joss.03979.

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35

Prosperi, Mattia, Yi Guo, Matt Sperrin, James S. Koopman, Jae S. Min, Xing He, Shannan Rich, Mo Wang, Iain E. Buchan, and Jiang Bian. "Causal inference and counterfactual prediction in machine learning for actionable healthcare." Nature Machine Intelligence 2, no. 7 (July 2020): 369–75. http://dx.doi.org/10.1038/s42256-020-0197-y.

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36

Leng, Siyang, Ziwei Xu, and Huanfei Ma. "Reconstructing directional causal networks with random forest: Causality meeting machine learning." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 9 (September 2019): 093130. http://dx.doi.org/10.1063/1.5120778.

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37

Pearl, Judea. "The seven tools of causal inference, with reflections on machine learning." Communications of the ACM 62, no. 3 (February 21, 2019): 54–60. http://dx.doi.org/10.1145/3241036.

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38

Jung, Hyekyung. "A case study of estimating a causal effect using machine learning with Bayesian Additive Regression Trees." Korean Society for Educational Evaluation 35, no. 2 (June 30, 2022): 355–77. http://dx.doi.org/10.31158/jeev.2022.35.2.355.

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The study aims to introduce a causal inference method using machine learning to general education researchers, and in particular, focus on the theory and practice of Bayesian Additive Regression Trees algorithm. To analyze the empirical data, public data from the Korean Children and Youth Panel Survey 2018 were used. For an illustrative purpose, this study estimated the causal effect of participation in activities related to self (personality) development on students’ life satisfaction and self-esteem and discussed the feasibility of the BART method in educational impact studies. The applicability of the BART-based machine learning causal inference technique in the field of education was discussed in comparison with model-based propensity score and causal effect estimation. Finally future research topics and limitations of the study were addressed.
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Ullal, Mithun S., Iqbal Thonse Hawaldar, Rashmi Soni, and Mohammed Nadeem. "The Role of Machine Learning in Digital Marketing." SAGE Open 11, no. 4 (October 2021): 215824402110503. http://dx.doi.org/10.1177/21582440211050394.

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Artificial Intelligence has been under researched. Machines with deep learning abilities can take digital marketing to new heights with their Artificial Intelligence making all the difference. This research aims to identify the outcomes from the study of Indian customer’s responses across varying demographics to machines and their abilities to sell, which will well be the future of digital marketing. We find that software developers need to build the architecture is partnership with digital marketers who use machines with deep learning by taking attitude of the customers, behavior and choices into consideration. This will unlock huge benefits to the companies as accurate information about customers will be easily available to the marketers in future. How the machines are going to perform under various conditions are explained using a causal model using regression models. SPSS version 24 and R software were used for analysing the data and data regarding the customer’s behaviors, their choices and emotions are collected and based on fuzzy-set qualitative comparative analysis (fsQCA) approach how they can be influenced to use the services of the machine, fsQCA is used to compare case oriented and variable oriented quantitative analysis.
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Park, Sung Bae, and Changwon Yoo. "Development of a graphical model of causal gene regulatory networks using medical big data and Bayesian machine learning." Journal of the Korean Medical Association 65, no. 3 (March 10, 2022): 167–72. http://dx.doi.org/10.5124/jkma.2022.65.3.167.

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Background: Data collection from medicine and biomedical science is becoming a large task and increasingly complicated with each passing day. Machine learning methods have been applied to elucidate interactions between genes and genes and their environment.Current Concepts: Many machine learning methods have been used to determine the statistical meaning or relationship in the prediction or progression of diseases through the creation of causal networks based on medical big data. Through these analyses, the occurrence and progression of diseases have been shown to be related to several genes and environmental factors. However, these methods cannot identify the key upstream regulators inferred from genomic, clinical, and environmental medical data.Discussion and Conclusion: The causal Bayesian network (CBN) is a machine learning method that can be used to understand a causal network inferred from the gene expression data. The CBN can help identify the key upstream regulators through examining the causal network inferred from medical big data having genomic information. We can easily improve the clinical outcome through regulation of these identified key upstream factors. Therefore, the CBN may be a powerful and flexible tool in the era of precision medicine.
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Sahoh, Bukhoree, Kanjana Haruehansapong, and Mallika Kliangkhlao. "Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model." IEEE Access 10 (2022): 24327–39. http://dx.doi.org/10.1109/access.2022.3155118.

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Zivich, Paul N., and Alexander Breskin. "Machine Learning for Causal Inference: On the Use of Cross-fit Estimators." Epidemiology 32, no. 3 (February 2, 2021): 393–401. http://dx.doi.org/10.1097/ede.0000000000001332.

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Graña, Manuel, Leire Ozaeta, and Darya Chyzhyk. "Dynamic Causal Modeling and machine learning for effective connectivity in Auditory Hallucination." Neurocomputing 326-327 (January 2019): 61–68. http://dx.doi.org/10.1016/j.neucom.2016.08.157.

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Choi, Byeong Yeob, Chen‐Pin Wang, and Jonathan Gelfond. "Machine learning outcome regression improves doubly robust estimation of average causal effects." Pharmacoepidemiology and Drug Safety 29, no. 9 (July 27, 2020): 1120–33. http://dx.doi.org/10.1002/pds.5074.

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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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Chalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. "Productivity and Selection of Human Capital with Machine Learning." American Economic Review 106, no. 5 (May 1, 2016): 124–27. http://dx.doi.org/10.1257/aer.p20161029.

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Economists have become increasingly interested in studying the nature of production functions in social policy applications, with the goal of improving productivity. Traditionally models have assumed workers are homogenous inputs. However, in practice, substantial variability in productivity means the marginal productivity of labor depends substantially on which new workers are hired--which requires not an estimate of a causal effect, but rather a prediction. We demonstrate that there can be large social welfare gains from using machine learning tools to predict worker productivity, using data from two important applications - police hiring and teacher tenure decisions.
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Athey, Susan, and Guido W. Imbens. "Machine Learning Methods That Economists Should Know About." Annual Review of Economics 11, no. 1 (August 2, 2019): 685–725. http://dx.doi.org/10.1146/annurev-economics-080217-053433.

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We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.
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Zhou, Wusheng. "Prediction of Urban and Rural Tourism Economic Forecast Based on Machine Learning." Scientific Programming 2021 (September 22, 2021): 1–7. http://dx.doi.org/10.1155/2021/4072499.

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With the rapid development of tourism, tourism revenue, as one of the important indicators to measure the development of the tourism economy, has high research value. The quasi-prediction of tourism revenue can drive the development of a series of related industries and accelerate the development of the domestic economy. When forecasting tourism income, it is necessary to examine the causal relationship between tourism income and local economic development. The traditional cointegration analysis method is to extract the promotion characteristics of tourism income to the local economy and construct a tourism income prediction model, but it cannot accurately describe the causal relationship between tourism income and local economic development and cannot accurately predict tourism income. We propose an optimized forecasting method of tourism revenue based on time series. This method first conducts a cointegration test on the time series data of the relationship between tourism income and local economic development, constructs a two-variable autoregressive model of tourism income and local economy, and uses the swarm intelligence method to test the causal relationship and the relationship between tourism income and local economic development, calculate the proportion of tourism industry, define the calculation result as the direct influence factor of tourism industry on the local economy, calculate the relevant effect of local tourism development and economic income, and construct tourism income optimization forecast model. The simulation results show that the model used can accurately predict tourism revenue.
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Langen, Henrika, and Martin Huber. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign." PLOS ONE 18, no. 1 (January 11, 2023): e0278937. http://dx.doi.org/10.1371/journal.pone.0278937.

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We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retailer. Besides assessing the average impacts of different types of coupons, we also investigate the heterogeneity of causal effects across different subgroups of customers, e.g., between clients with relatively high vs. low prior purchases. Finally, we use optimal policy learning to determine (in a data-driven way) which customer groups should be targeted by the coupon campaign in order to maximize the marketing intervention’s effectiveness in terms of sales. We find that only two out of the five coupon categories examined, namely coupons applicable to the product categories of drugstore items and other food, have a statistically significant positive effect on retailer sales. The assessment of group average treatment effects reveals substantial differences in the impact of coupon provision across customer groups, particularly across customer groups as defined by prior purchases at the store, with drugstore coupons being particularly effective among customers with high prior purchases and other food coupons among customers with low prior purchases. Our study provides a use case for the application of causal machine learning in business analytics to evaluate the causal impact of specific firm policies (like marketing campaigns) for decision support.
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Lemmens, Aurélie, and Sunil Gupta. "Managing Churn to Maximize Profits." Marketing Science 39, no. 5 (September 2020): 956–73. http://dx.doi.org/10.1287/mksc.2020.1229.

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