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Journal articles on the topic 'Structural causal models'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Beckers, Sander. "Equivalent Causal Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 6202–9. http://dx.doi.org/10.1609/aaai.v35i7.16771.

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The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The idea is that two models are equivalent when they agree on all "essential" causal information that can be expressed using their common variables. I do so by focussing on the two main features of causal models, namely their structural relations and their functional relations. In particular, I define several relations of causal ancestry and several relations of causal sufficiency, and require that the most general of these relations are preserved across equivalent models.

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2

Vansteelandt, S., and E. Goetghebeur. "Causal inference with generalized structural mean models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, no. 4 (October 28, 2003): 817–35. http://dx.doi.org/10.1046/j.1369-7412.2003.00417.x.

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3

HUBER, FRANZ. "STRUCTURAL EQUATIONS AND BEYOND." Review of Symbolic Logic 6, no. 4 (July 8, 2013): 709–32. http://dx.doi.org/10.1017/s175502031300018x.

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AbstractRecent accounts of actual causation are stated in terms of extended causal models. These extended causal models contain two elements representing two seemingly distinct modalities. The first element are structural equations which represent the “(causal) laws” or mechanisms of the model, just as ordinary causal models do. The second element are ranking functions which represent normality or typicality. The aim of this paper is to show that these two modalities can be unified. I do so by formulating two constraints under which extended causal models with their two modalities can be subsumed under so called “counterfactual models” which contain just one modality. These two constraints will be formally precise versions of Lewis’ (1979) familiar “system of weights or priorities” governing overall similarity between possible worlds.

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4

Robins, James M., Miguel Ángel Hernán, and Babette Brumback. "Marginal Structural Models and Causal Inference in Epidemiology." Epidemiology 11, no. 5 (September 2000): 550–60. http://dx.doi.org/10.1097/00001648-200009000-00011.

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5

Rothenhäusler, Dominik, Jan Ernest, and Peter Bühlmann. "Causal inference in partially linear structural equation models." Annals of Statistics 46, no. 6A (December 2018): 2904–38. http://dx.doi.org/10.1214/17-aos1643.

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6

Neugebauer, Romain, and Mark van der Laan. "Nonparametric causal effects based on marginal structural models." Journal of Statistical Planning and Inference 137, no. 2 (February 2007): 419–34. http://dx.doi.org/10.1016/j.jspi.2005.12.008.

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7

Zheng, Cheng, David C. Atkins, Xiao-Hua Zhou, and Isaac C. Rhew. "Causal Models for Mediation Analysis: An Introduction to Structural Mean Models." Multivariate Behavioral Research 50, no. 6 (November 2, 2015): 614–31. http://dx.doi.org/10.1080/00273171.2015.1070707.

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8

Talbot, Denis, Amanda M. Rossi, Simon L. Bacon, Juli Atherton, and Geneviève Lefebvre. "A graphical perspective of marginal structural models: An application for the estimation of the effect of physical activity on blood pressure." Statistical Methods in Medical Research 27, no. 8 (December 29, 2016): 2428–36. http://dx.doi.org/10.1177/0962280216680834.

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Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models are a relatively new class of causal models that effectively deal with the estimation of the effects of time-varying exposures. Marginal structural models have traditionally been embedded in the counterfactual framework to causal inference. In this paper, we use the causal graph framework to enhance the implementation of marginal structural models. We illustrate our approach using data from a prospective cohort study, the Honolulu Heart Program. These data consist of 8006 men at baseline. To illustrate our approach, we focused on the estimation of the causal effect of physical activity on blood pressure, which were measured at three time points. First, a causal graph is built to encompass prior knowledge. This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using marginal structural models for repeated measures and guided the implementation of the models with the causal graph. By employing the causal graph framework, we also show the validity of fitting conditional marginal structural models for repeated measures in the context implied by our data.

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Bazinas, Vassilios, and Bent Nielsen. "Causal Transmission in Reduced-Form Models." Econometrics 10, no. 2 (March 24, 2022): 14. http://dx.doi.org/10.3390/econometrics10020014.

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We propose a method to explore the causal transmission of an intervention through two endogenous variables of interest. We refer to the intervention as a catalyst variable. The method is based on the reduced-form system formed from the conditional distribution of the two endogenous variables given the catalyst. The method combines elements from instrumental variable analysis and Cholesky decomposition of structural vector autoregressions. We give conditions for uniqueness of the causal transmission.

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10

Steyer, Rolf. "Analyzing Individual and Average Causal Effects via Structural Equation Models." Methodology 1, no. 1 (January 2005): 39–54. http://dx.doi.org/10.1027/1614-1881.1.1.39.

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Abstract. Although both individual and average causal effects are defined in Rubin's approach to causality, in this tradition almost all papers center around learning about the average causal effects. Almost no efforts deal with developing designs and models to learn about individual effects. This paper takes a first step in this direction. In the first and general part, Rubin's concepts of individual and average causal effects are extended replacing Rubin's deterministic potential-outcome variables by the stochastic expected-outcome variables. Based on this extension, in the second and main part specific designs, assumptions and models are introduced which allow identification of (1) the variance of the individual causal effects, (2) the regression of the individual causal effects on the true scores of the pretests, (3) the regression of the individual causal effects on other explanatory variables, and (4) the individual causal effects themselves. Although random assignment of the observational unit to one of the treatment conditions is useful and yields stronger results, much can be achieved with a nonequivalent control group. The simplest design requires two pretests measuring a pretest latent trait that can be interpreted as the expected outcome under control, and two posttests measuring a posttest latent trait: The expected outcome under treatment. The difference between these two latent trait variables is the individual-causal-effect variable, provided some assumptions can be made. These assumptions - which rule out alternative explanations in the Campbellian tradition - imply a single-trait model (a one-factor model) for the untreated control condition in which no treatment takes place, except for change due to measurement error. These assumptions define a testable model. More complex designs and models require four occasions of measurement, two pretest occasions and two posttest occasions. The no-change model for the untreated control condition is then a single-trait-multistate model allowing for measurement error and occasion-specific effects.

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11

Holland, Paul W. "Causal Inference, Path Analysis, and Recursive Structural Equations Models." Sociological Methodology 18 (1988): 449. http://dx.doi.org/10.2307/271055.

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12

Bikak, Marvi. "Structural causal models’ application in development of clinical trials." Southwest Respiratory and Critical Care Chronicles 7, no. 31 (November 3, 2019): 1–2. http://dx.doi.org/10.12746/swrccc.v7i31.587.

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13

Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. "Inferring causal impact using Bayesian structural time-series models." Annals of Applied Statistics 9, no. 1 (March 2015): 247–74. http://dx.doi.org/10.1214/14-aoas788.

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14

Holland, Paul W. "CAUSAL INFERENCE, PATH ANALYSIS AND RECURSIVE STRUCTURAL EQUATIONS MODELS." ETS Research Report Series 1988, no. 1 (June 1988): i—50. http://dx.doi.org/10.1002/j.2330-8516.1988.tb00270.x.

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15

Halpern, Joseph Y., and Spencer Peters. "Reasoning about Causal Models with Infinitely Many Variables." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5668–75. http://dx.doi.org/10.1609/aaai.v36i5.20508.

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Generalized structural equations models (GSEMs) (Peters and Halpern 2021), are, as the name suggests, a generalization of structural equations models (SEMs). They can deal with (among other things) infinitely many variables with infinite ranges, which is critical for capturing dynamical systems. We provide a sound and complete axiomatization of causal reasoning in GSEMs that is an extension of the sound and complete axiomatization provided by Halpern (2000) for SEMs. Considering GSEMs helps clarify what properties Halpern's axioms capture.

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Bauer, Daniel J. "Estimating Multilevel Linear Models as Structural Equation Models." Journal of Educational and Behavioral Statistics 28, no. 2 (June 2003): 135–67. http://dx.doi.org/10.3102/10769986028002135.

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Multilevel linear models (MLMs) provide a powerful framework for analyzing data collected at nested or non-nested levels, such as students within classrooms. The current article draws on recent analytical and software advances to demonstrate that a broad class of MLMs may be estimated as structural equation models (SEMs). Moreover, within the SEM approach it is possible to include measurement models for predictors or outcomes, and to estimate the mediational pathways among predictors explicitly, tasks which are currently difficult with the conventional approach to multilevel modeling. The equivalency of the SEM approach with conventional methods for estimating MLMs is illustrated using empirical examples, including an example involving both multiple indicator latent factors for the outcomes and a causal chain for the predictors. The limitations of this approach for estimating MLMs are discussed and alternative approaches are considered.

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Bollen. "Evaluating Effect, Composite, and Causal Indicators in Structural Equation Models." MIS Quarterly 35, no. 2 (2011): 359. http://dx.doi.org/10.2307/23044047.

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Tanaka, Shiro, M. Alan Brookhart, and Jason P. Fine. "G-estimation of structural nested mean models for competing risks data using pseudo-observations." Biostatistics 21, no. 4 (May 6, 2019): 860–75. http://dx.doi.org/10.1093/biostatistics/kxz015.

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Summary This article provides methods of causal inference for competing risks data. The methods are formulated as structural nested mean models of causal effects directly related to the cumulative incidence function or subdistribution hazard, which reflect the survival experience of a subject in the presence of competing risks. The effect measures include causal risk differences, causal risk ratios, causal subdistribution hazard ratios, and causal effects of time-varying exposures. Inference is implemented by g-estimation using pseudo-observations, a technique to handle censoring. The finite-sample performance of the proposed estimators in simulated datasets and application to time-varying exposures in a cohort study of type 2 diabetes are also presented.

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19

Magrini, Alessandro. "Linear Markovian models for lag exposure assessment." Biometrical Letters 55, no. 2 (December 1, 2018): 179–95. http://dx.doi.org/10.2478/bile-2018-0012.

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SummaryLinear regression with temporally delayed covariates (distributed-lag linear regression) is a standard approach to lag exposure assessment, but it is limited to a single biomarker of interest and cannot provide insights on the relationships holding among the pathogen exposures, thus precluding the assessment of causal effects in a general context. In this paper, to overcome these limitations, distributed-lag linear regression is applied to Markovian structural causal models. Dynamic causal effects are defined as a function of regression coefficients at different time lags. The proposed methodology is illustrated using a simple lag exposure assessment problem.

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20

Hu, Yaowei, Yongkai Wu, Lu Zhang, and Xintao Wu. "A Generative Adversarial Framework for Bounding Confounded Causal Effects." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 12104–12. http://dx.doi.org/10.1609/aaai.v35i13.17437.

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Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounding based on Pearl's structural causal model. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, using an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution, and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make assumption about the type of structural equations and variables. Experiments using both synthetic and real-world datasets are conducted.

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21

Mayer, Axel. "Causal Effects Based on Latent Variable Models." Methodology 15, Supplement 1 (October 1, 2019): 15–28. http://dx.doi.org/10.1027/1614-2241/a000174.

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Abstract. Building on the stochastic theory of causal effects and latent state-trait theory, this article shows how a comprehensive analysis of the effects of interventions can be conducted based on latent variable models. The proposed approach offers new ways to evaluate the differential effects of interventions for substantive researchers in experimental and observational studies while allowing for complex measurement models. The key definitions and assumptions of the stochastic theory of causal effects are first introduced and then four statistical models that can be used to estimate various types of causal effects with latent state-trait models are developed and illustrated: The multistate effect model with and without method factors, the true-change effect model, and the multitrait effect model. All effect models with latent variables are implemented based on multigroup structural equation modeling with the EffectLiteR approach. Particular emphasis is placed on the development of models with interactions that allow for interindividual differences in treatment effects based on latent variables. Open source software code is provided for all models.

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22

Loeys, Tom, and Els Goetghebeur. "80 Structural failure time models for causal inference in clinical trials." Controlled Clinical Trials 18, no. 3 (June 1997): S91. http://dx.doi.org/10.1016/s0197-2456(97)91065-5.

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23

Neugebauer, Romain, Mark J. van der Laan, Marshall M. Joffe, and Ira B. Tager. "Causal inference in longitudinal studies with history-restricted marginal structural models." Electronic Journal of Statistics 1 (2007): 119–54. http://dx.doi.org/10.1214/07-ejs050.

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24

Clarke, P. S., and F. Windmeijer. "Identification of causal effects on binary outcomes using structural mean models." Biostatistics 11, no. 4 (June 3, 2010): 756–70. http://dx.doi.org/10.1093/biostatistics/kxq024.

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25

Rahmadi, Ridho, Perry Groot, Marieke HC van Rijn, Jan AJG van den Brand, Marianne Heins, Hans Knoop, and Tom Heskes. "Causality on longitudinal data: Stable specification search in constrained structural equation modeling." Statistical Methods in Medical Research 27, no. 12 (June 28, 2017): 3814–34. http://dx.doi.org/10.1177/0962280217713347.

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A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

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Magrini, Alessandro. "Distributed-lag Linear Structural Equation Models in R: the dlsem Package." Austrian Journal of Statistics 48, no. 2 (January 26, 2019): 14–42. http://dx.doi.org/10.17713/ajs.v48i2.777.

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In this paper, an extension of linear Markovian structural causal models is introduced,called distributed-lag linear structural equation models (DLSEMs),where each factor of the joint probability distribution is adistributed-lag linear regression with constrained lag shapes.DLSEMs account for temporal delays in the dependence relationshipsamong the variables and allow to assess dynamic causal effects.As such, they represent a suitable methodology to investigate the effectof an external impulse on a multidimensional system through time.In this paper, we present the dlsem package for Rimplementing inference functionalities for DLSEMs.The use of the package is illustrated through an example on simulated dataand a real-world application aiming at assessing the impact of agriculturalresearch expenditure on multiple dimensions in Europe.

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Brick, Timothy R., and Drew H. Bailey. "Rock the MIC: The Matrix of Implied Causation, a Tool for Experimental Design and Model Checking." Advances in Methods and Practices in Psychological Science 3, no. 3 (June 25, 2020): 286–99. http://dx.doi.org/10.1177/2515245920922775.

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Path modeling and the extended structural equation modeling framework are in increasingly common use for statistical analysis in modern behavioral science. Path modeling, including structural equation modeling, provides a flexible means of defining complex models in a way that allows them to be easily visualized, specified, and fitted to data. Although causality cannot be determined simply by fitting a path model, researchers often use such models as representations of underlying causal-process models. Indeed, causal implications are a vital characteristic of a model’s explanatory value, but these implications are rarely examined directly. When models are hypothesized to be causal, they can be differentiated from one another by examining their causal implications as defined by a combination of the model assumptions, data, and estimation procedure. However, the implied causal relationships may not be immediately obvious to researchers, especially for intricate or long-chain causal structures (as in longitudinal panel designs). We introduce the matrix of implied causation (MIC) as a tool for easily understanding and reporting a model’s implications for the causal influence of one variable on another. With examples from the literature, we illustrate the use of MICs in model checking and experimental design. We argue that MICs should become a routine element of interpretation when models with complex causal implications are examined, and that they may provide an additional tool for differentiating among models with otherwise similar fit.

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Qiao, Jie, Ruichu Cai, Kun Zhang, Zhenjie Zhang, and Zhifeng Hao. "Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models." ACM Transactions on Intelligent Systems and Technology 12, no. 6 (December 31, 2021): 1–28. http://dx.doi.org/10.1145/3482879.

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Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class does not allow any confounding or intermediate variables between a cause pair–even if each direct causal relation follows this model. However, omitting the latent causal variables is frequently encountered in practice. After the omission, the model does not necessarily follow the model constraints. As a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a confounding cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured confounding and intermediate variables, from data under the variational auto-encoder framework. Our theoretical results show that with our model, the causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.

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Dorfman, Leonid Ya, and Alexey Yu Kalugin. "A CAUSAL FOOTPRINT IN INDIVIDUAL-INTELLECTUAL INTEGRATIONS. PART 2. CAUSAL CHAINS AND LONGITUDINAL MEDIATOR MODELS." Вестник Пермского университета. Философия. Психология. Социология, no. 4 (2022): 597–608. http://dx.doi.org/10.17072/2078-7898/2022-4-597-608.

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In the previous report, the authors described three possible lines of integration: theoretical and empirical joint-ly, cross-theoretical and cross-empirical. In this report, their empirical verification examines on the example of V.S. Merlin’s theory of integral individuality, D.V. Ushakov’s structural-dynamic theory of intelligence, and J. Guilford’s theory of divergent (creative) thinking. Individual-intellectual integrations derive from above the-ories. These integrations appear due to the viewpoint of a causal chain. Its operational marker is longitudinal mediator models. The purpose of the study is to examine functionally the resource and potential of the theories of V.S. Merlin, D.V. Ushakov and J. Guilford for integration at empirical level; individual-intellectual integra-tions develop following causal chains in three time periods. Longitudinal mediation models were to test with an emphasis on the transformation of the effects of previous causes into new causes. The study involved 211 students of Perm higher educational institutions, studying humanities, of which 161 girls and 50 boys aged 17 to 22 years at the time of the first measurement (M = 18.56, SD = 0.83). We tested traits of integral individuali-ty (nervous system, temperament, and personality traits), crystallized and fluid intelligence, and creative think-ing in three periods, namely, the present, the future, and the post-future. Structural modeling was the main sta-tistical methods of data analysis. Crystallized intelligence, fluid intelligence, and creative flexibility revealed their mediator part between traits of integral individuality in earlier and later measurements. Each model was fit indices. However, creative fluency and originality did not function as mediators. Rather, they acted as the environment on which longitudinal mediator models operate. The results of the study can form the background for the development of a program of comprehensive individual-intellectual integrations as the basis of the edu-cational capital of the individual.

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30

Castillo, E., A. Cobo, J. M. Gutiérrez, A. Iglesias, and H. Sagástegui. "Causal Network Models in Expert Systems." Computer-Aided Civil and Infrastructure Engineering 9, no. 5 (September 1994): 315–28. http://dx.doi.org/10.1111/j.1467-8667.1994.tb00339.x.

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Tank, A., E. B. Fox, and A. Shojaie. "Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series." Biometrika 106, no. 2 (April 8, 2019): 433–52. http://dx.doi.org/10.1093/biomet/asz007.

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Summary Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale, we take a model-based approach that relies on structural vector autoregressive models. We present a unifying framework for parameter identifiability and estimation under subsampling and mixed frequencies when the noise, or shocks, is non-Gaussian. By studying the structural case, we develop identifiability and estimation methods for the causal structure of lagged and instantaneous effects at the desired time scale. We further derive an exact expectation-maximization algorithm for inference in both subsampled and mixed-frequency settings. We validate our approach in simulated scenarios and on a climate and an econometric dataset.

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Kang, Jin-Su, Stephen Thomas Downing, Nabangshu Sinha, and Yi-Chieh Chen. "Advancing Causal Inference: Differences-in-Differences vs. Bayesian Structural Time Series Models." Academy of Management Proceedings 2021, no. 1 (August 2021): 15410. http://dx.doi.org/10.5465/ambpp.2021.15410abstract.

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Hernán, Miguel A., Babette Brumback, and James M. Robins. "Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments." Journal of the American Statistical Association 96, no. 454 (June 2001): 440–48. http://dx.doi.org/10.1198/016214501753168154.

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Penny, W. D., K. E. Stephan, A. Mechelli, and K. J. Friston. "Modelling functional integration: a comparison of structural equation and dynamic causal models." NeuroImage 23 (January 2004): S264—S274. http://dx.doi.org/10.1016/j.neuroimage.2004.07.041.

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Cohen, Patricia, Jacob Cohen, Jeanne Teresi, Margaret Marchi, and C. Noemi Velez. "Problems in the Measurement of Latent Variables in Structural Equations Causal Models." Applied Psychological Measurement 14, no. 2 (June 1990): 183–96. http://dx.doi.org/10.1177/014662169001400207.

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Matsouaka, Roland A., and Eric J. Tchetgen Tchetgen. "Instrumental variable estimation of causal odds ratios using structural nested mean models." Biostatistics 18, no. 3 (February 6, 2017): 465–76. http://dx.doi.org/10.1093/biostatistics/kxw059.

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37

Low, Hamish, and Costas Meghir. "The Use of Structural Models in Econometrics." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 33–58. http://dx.doi.org/10.1257/jep.31.2.33.

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This paper discusses the role of structural economic models in empirical analysis and policy design. The central payoff of a structural econometric model is that it allows an empirical researcher to go beyond the conclusions of a more conventional empirical study that provides reduced-form causal relationships. Structural models identify mechanisms that determine outcomes and are designed to analyze counterfactual policies, quantifying impacts on specific outcomes as well as effects in the short and longer run. We start by defining structural models, distinguishing between those that are fully specified and those that are partially specified. We contrast the treatment effects approach with structural models, and present an example of how a structural model is specified and the particular choices that were made. We cover combining structural estimation with randomized experiments. We then turn to numerical techniques for solving dynamic stochastic models that are often used in structural estimation, again with an example. The penultimate section focuses on issues of estimation using the method of moments.

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Hidalgo-Contreras, Juan Valente, Josafhat Salinas-Ruiz, Kent M. Eskridge, and Stephen P. Baenziger. "Incorporating Molecular Markers and Causal Structure among Traits Using a Smith-Hazel Index and Structural Equation Models." Agronomy 11, no. 10 (September 28, 2021): 1953. http://dx.doi.org/10.3390/agronomy11101953.

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The goal in breeding programs is to choose candidates that produce offspring with the best phenotypes. In conventional selection, the best candidate is selected with high genotypic values (unobserved), in the assumption that this is related to the observed phenotypic values for several traits. Multi-trait selection indices are used to identify superior genotypes when a number of traits are to be considered simultaneously. Often, the causal relationship among the traits is well known. Structural equation models (SEM) have been used to describe the causal relationships among variables in many biological systems. We present a method for multi-trait genomic selection that incorporates causal relationships among traits by coupling SEM with a Smith–Hazel index that incorporates markers. The method was applied to field data from a Nebraska winter wheat breeding program. We found that the correlation and the relative efficiency increased for the proposed Smith–Hazel indices when the total causal information among traits was accounted for by the vector of weights (b), which includes the causal path coefficients in the causal matrix (Λ). On the other hand, when selection was based on a primary trait, for example yield, the proposed SI increased the mean yield of the best 28 (Top 10%) genotypes to 7%.

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HALPERN, JOSEPH Y. "APPROPRIATE CAUSAL MODELS AND THE STABILITY OF CAUSATION." Review of Symbolic Logic 9, no. 1 (January 7, 2016): 76–102. http://dx.doi.org/10.1017/s1755020315000246.

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AbstractCausal models defined in terms of structural equations have proved to be quite a powerful way of representing knowledge regarding causality. However, a number of authors have given examples that seem to show that the Halpern–Pearl (HP) definition of causality (Halpern & Pearl, 2005) gives intuitively unreasonable answers. Here it is shown that, for each of these examples, we can give two stories consistent with the description in the example, such that intuitions regarding causality are quite different for each story. By adding additional variables, we can disambiguate the stories. Moreover, in the resulting causal models, the HP definition of causality gives the intuitively correct answer. It is also shown that, by adding extra variables, a modification to the original HP definition made to deal with an example of Hopkins & Pearl (2003) may not be necessary. Given how much can be done by adding extra variables, there might be a concern that the notion of causality is somewhat unstable. Can adding extra variables in a “conservative” way (i.e., maintaining all the relations between the variables in the original model) cause the answer to the question “Is X = x a cause of Y = y?” to alternate between “yes” and “no”? It is shown that we can have such alternation infinitely often, but if we take normality into consideration, we cannot. Indeed, under appropriate normality assumptions. Adding an extra variable can change the answer from “yes’ to “no”, but after that, it cannot change back to “yes”.

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Damonte, Alessia. "Modeling configurational explanations." Italian Political Science Review/Rivista Italiana di Scienza Politica 51, no. 2 (February 16, 2021): 182–97. http://dx.doi.org/10.1017/ipo.2021.2.

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AbstractHow can Qualitative Comparative Analysis contribute to causal knowledge? The article's answer builds on the shift from design to models that the Structural Causal Model framework has compelled in the probabilistic analysis of causation. From this viewpoint, models refine the claim that a ‘treatment’ has causal relevance as they specify the ‘covariates’ that make some units responsive. The article shows how QCA can establish configurational models of plausible ‘covariates’. It explicates the rationale, operations, and criteria that confer explanatory import to configurational models, then illustrates how the basic structures of the SCM can widen the interpretability of configurational solutions and deepen the dialogue among techniques.

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Stephenson, Victoria, Chris J. Oates, Andrew Finlayson, Chris Thomas, and Kevin J. Wilson. "Causal Graphical Models for Systems-Level Engineering Assessment." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7, no. 2 (June 2021): 04021011. http://dx.doi.org/10.1061/ajrua6.0001116.

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Zhou, Xiang, and Geoffrey T. Wodtke. "Residual Balancing: A Method of Constructing Weights for Marginal Structural Models." Political Analysis 28, no. 4 (March 4, 2020): 487–506. http://dx.doi.org/10.1017/pan.2020.2.

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When making causal inferences, post-treatment confounders complicate analyses of time-varying treatment effects. Conditioning on these variables naively to estimate marginal effects may inappropriately block causal pathways and may induce spurious associations between treatment and the outcome, leading to bias. To avoid such bias, researchers often use marginal structural models (MSMs) with inverse probability weighting (IPW). However, IPW requires models for the conditional distributions of treatment and is highly sensitive to their misspecification. Moreover, IPW is relatively inefficient, susceptible to finite-sample bias, and difficult to use with continuous treatments. We introduce an alternative method of constructing weights for MSMs, which we call “residual balancing”. In contrast to IPW, it requires modeling the conditional means of the post-treatment confounders rather than the conditional distributions of treatment, and it is therefore easier to use with continuous treatments. Numeric simulations suggest that residual balancing is both more efficient and more robust to model misspecification than IPW and its variants in a variety of scenarios. We illustrate the method by estimating (a) the cumulative effect of negative advertising on election outcomes and (b) the controlled direct effect of shared democracy on public support for war. Open-source software is available for implementing the proposed method.

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Héroux, Julie, Erica E. M. Moodie, Erin Strumpf, Natalie Coyle, Pierre Tousignant, and Mamadou Diop. "Marginal structural models for skewed outcomes: identifying causal relationships in health care utilization." Statistics in Medicine 33, no. 7 (October 24, 2013): 1205–21. http://dx.doi.org/10.1002/sim.6020.

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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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Yang, Hye-Won, and Sun-Geun Baek. "Causal Path Analysis of Teaching Competency based on the Structural Causal Model: Focusing on the Comparison across South Korea, England, and Finland using TALIS 2018 data." Korean Society for Educational Evaluation 35, no. 4 (December 31, 2022): 657–86. http://dx.doi.org/10.31158/jeev.2022.35.4.657.

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Based on the structural causal model, this study derived a causal graph that shows the causal relationship between the factors predicting the teaching competency of lower secondary school teachers in South Korea, the UK(England), and Finland. Also, it compared and analyzed the causal path to each country’s teaching competency. To this end, the data of lower secondary school teachers and principals, who participated in TALIS 2018, in Korea, the UK(England), and Finland were analyzed. First, the top 20 factors that predict teaching competency by each country were extracted by applying the mixed-effect random forest technique in consideration of the multi-layer structure of the data. Then, the causal graphs were derived by applying the causal discovery algorithm based on a structural causal model with the extracted predictors. As a result, there were common factors and discrimination factors in the top 20 predictors extracted from each national data, and the causal paths to teaching competency were compared and analyzed in the context of each country based on the causal graph by country. In addition, in the field of education, the possibility of using causal inference based on structural causal models was discussed, and the limitations and implications of this study were presented.

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Mohammadi, Yahya, Davoud Ali Saghi, Ali Reza Shahdadi, Guilherme Jordão de Magalhães Rosa, and Morteza Sattaei Mokhtari. "Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep." Acta Scientiarum. Animal Sciences 42 (June 8, 2020): e48823. http://dx.doi.org/10.4025/actascianimsci.v42i1.48823.

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Data collected on 2550 Kurdi lambs originated from 1505 dams and 149 sires during 1991 to 2015 in Hossein Abad Kurdi Sheep Breeding Station, located in Shirvan city, North Khorasan province, North-eastern area of Iran, were used for inferring causal relationship among the body weights at birth (BW), at weaning (WW), at six-month age (6MW), at nine-month age (9MW) and yearling age (YW). The inductive causation (IC) algorithm was employed to search for causal structure among these traits. This algorithm was applied to the posterior distribution of the residual (co)variance matrix of a standard multivariate model (SMM). The causal structure detected by the IC algorithm coupling with biological prior knowledge provides a temporal recursive causal network among the studied traits. The studied traits were analyzed under three multivariate models including SMM, fully recursive multivariate model (FRM) and IC-based multivariate model (ICM) via a Bayesian approach by 100,000 iterations, thinning interval of 10 and the first 10,000 iterations as burn-in. The three considered multivariate models (SMM, FRM and ICM) were compared using deviance information criterion (DIC) and predictive ability measures including mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y, )) of records. In general, structural equation based models (FRM and ICM) performed better than SMM in terms of lower DIC and MSE and also higher r(y, ). Among the tested models ICM had the lowest (36678.551) and SMM had the highest (36744.107)DIC values. In each case of the traits studied, the lowest MSE and the highest r(y, ) were obtained under ICM. The causal effects of BW on WW, WW on 6MW, 6MW on 9MW and 9MW on YW were statistically significant values of 1.478, 0.737, 0.776 and 0.929 kg, respectively (99% highest posterior density intervals did not include zero).

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Hayduk, Leslie. "Review essay on Rex B. Kline’s Principles and Practice of Structural Equation Modeling: Encouraging a fifth edition." Canadian Studies in Population 45, no. 3-4 (August 30, 2018): 154. http://dx.doi.org/10.25336/csp29397.

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Principles and Practice of Structural Equation Modeling, 4th editionRex B. KlineNew York: The Guilford Press 2016ISBN 978-1-4625-2334-4Softcover, US$65, 534 pp.Kline’s fourth edition is reasonably strong but improvable. The text aims to introduce newcomers to fundamental structural equation modeling (SEM) principles, but tends to confuse “Principles” with “Rules.” Rules having insufficient grounding in principles leave readers ill-prepared for understanding and responding to changes in previously traditional “rules”—such as those concerning model testing, and latents having single indicators. SEM’s foundations would be clearer if Kline began by presenting structural equation models as striving to represent causal effects—a commitment that differentiates structural equation models from regression and encourages model testing. I begin this review by summarizing the covariance/correlation implications of three simple causal structures, which pinpoints multiple text improvements and underpins the discussions of measurement and model testing that follow. Causal structuring also grounds my later comments regarding modelling means/intercepts and interactions. A file of Supplementary Sections expands on several points and lists multiple editorial corrections you might pencil into your copy of Kline’s text.

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Torres, Michelle. "Estimating controlled direct effects through marginal structural models." Political Science Research and Methods 8, no. 3 (February 13, 2020): 391–408. http://dx.doi.org/10.1017/psrm.2020.3.

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AbstractWhen working with panel data, many researchers wish to estimate the direct effects of time-varying factors on future outcomes. However, when a baseline treatment affects both the confounders of further stages of the treatment and the outcome, the estimation of controlled direct effects (CDEs) using traditional regression methods faces a bias trade-off between confounding bias and post-treatment control. Drawing on research from the field of epidemiology, in this article I present a marginal structural modeling (MSM) approach that allows scholars to generate unbiased estimates of CDEs. Further, I detail the characteristics and implementation of MSMs, compare the performance of this approach under different conditions, and discuss and assess practical challenges when conducting them. After presenting the method, I apply MSMs to estimate the effect of wealth in childhood on political participation, highlighting the improvement in terms of bias relative to traditional regression models. The analysis shows that MSMs improve our understanding of causal mechanisms especially when dealing with multi-categorical time-varying treatments and non-continuous outcomes.

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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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Chupin, Guerric, and Henrik Nilsson. "Modular Compilation for a Hybrid Non-Causal Modelling Language." Electronics 10, no. 7 (March 30, 2021): 814. http://dx.doi.org/10.3390/electronics10070814.

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Non-causal modelling is a powerful approach to modelling physical systems in a variety of domains from science and engineering. Non-causal modelling languages enable a high-level and modular approach to modelling. However, it is hard to compile non-causal languages modularly (in the sense of separate compilation). This causes difficulties when simulating large models for which code generation takes a long time, or structurally singular models in which parts of the model are allowed to change at runtime. In this work, we introduce a technique we call order-parametric differentiation to allow truly modular compilation. The idea is to generate (machine) code that can compute derivatives of any order of an expression as needed, thus allowing for ahead-of-time modular compilation of a hybrid non-causal language. We also develop a compilation scheme that enables using partial models as first-class objects in a seamless way and simulating them without the need for just-in-time compilation, even in the presence of structural dynamism. We present a performance evaluation of the scheme we used and study its shortcomings and possible improvements, demonstrating that it is a feasible complement to existing implementation techniques for cases where true modular compilation is a primary objective.

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