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Abstract

This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an estimation strategy, can be effectively combined with causal inference, which has been traditionally concerned with identification. The incorporation of machine learning in causal inference enables researchers to better address potential biases in estimating causal effects and uncover heterogeneous causal effects. Uncovering sources of effect heterogeneity is key for generalizing to populations beyond those under study. While sociology has long emphasized the importance of causal mechanisms, historical and life-cycle variation, and social contexts involving network interactions, recent conceptual and computational advances facilitate more principled estimation of causal effects under these settings. We encourage sociologists to incorporate these insights into their empirical research.

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2023-07-31
2024-12-03
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Literature Cited

  1. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
    [Google Scholar]
  2. Abadie A, Imbens GW. 2016. Matching on the estimated propensity score. Econometrica 84:781–807
    [Google Scholar]
  3. Acharya A, Blackwell M, Sen M. 2018. Analyzing causal mechanisms in survey experiments. Political Anal. 26:357–78
    [Google Scholar]
  4. Ahearn C, Brand JE, Zhou X. 2022. How, and for whom, does higher education increase voting?. Res. High. Educ. https://doi.org/10.1007/s11162-022-09717-4
    [Google Scholar]
  5. Alwin DF, Hauser RM. 1975. The decomposition of effects in path analysis. Am. Sociol. Rev. 40:37–47
    [Google Scholar]
  6. An W 2010. Bayesian propensity score estimators: incorporating uncertainties in propensity scores into causal inference. Sociol. Methodol. 40:151–89
    [Google Scholar]
  7. An W 2018. Causal inference with networked treatment diffusion. Sociol. Methodol. 48:152–81
    [Google Scholar]
  8. An W, VanderWeele TJ. 2022. Opening the blackbox of treatment interference: tracing treatment diffusion through network analysis. Sociol. Methods Res. 51:141–64
    [Google Scholar]
  9. An W, Winship C. 2017. Causal inference in panel data with application to estimating race-of-interviewer effects in the general social survey. Sociol. Methods Res. 46:68–102
    [Google Scholar]
  10. Angrist JD, Imbens GW, Rubin DB. 1996. Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91:444–55
    [Google Scholar]
  11. Angrist JD, Pischke J-S. 2009. Mostly Harmless Econometrics: An Empiricist's Companion Princeton, NJ: Princeton Univ. Press
    [Google Scholar]
  12. Angrist JD, Rokkanen M. 2015. Wanna get away? Regression discontinuity estimation of exam school effects away from the cutoff. J. Am. Stat. Assoc. 110:1331–44
    [Google Scholar]
  13. Aronow PM, Samii C. 2016. Does regression produce representative estimates of causal effects?. Am. J. Political Sci. 60:250–67
    [Google Scholar]
  14. Aronow PM, Samii C. 2017. Estimating average causal effects under general interference, with application to a social network experiment. Ann. Appl. Stat. 11:1912–47
    [Google Scholar]
  15. Athey S, Eckles D, Imbens GW. 2018a. Exact p-values for network interference. J. Am. Stat. Assoc. 113:230–240
    [Google Scholar]
  16. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
    [Google Scholar]
  17. Athey S, Imbens G. 2017. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
    [Google Scholar]
  18. Athey S, Imbens G. 2019. Machine learning methods that economists should know about. Annu. Rev. Econ. 11:685–725
    [Google Scholar]
  19. Athey S, Imbens G, Wager S. 2018b. Approximate residual balancing: debiased inference of average treatment effects in high dimensions. J. R. Stat. Soc. Ser. B 80:597–623
    [Google Scholar]
  20. Athey S, Tibshirani J, Wager S. 2019. Generalized random forests. Ann. Stat. 47:21148–78
    [Google Scholar]
  21. Athey S, Wager S. 2021. Policy learning with observational data. Econometrics 89:1133–61
    [Google Scholar]
  22. Austin PC, Stuart EA. 2017. Estimating the effect of treatment on binary outcomes using full matching on the propensity score. Stat. Methods Med. Res. 26:2505–25
    [Google Scholar]
  23. Avin C, Shpitser I, Pearl J. 2005. Identifiability of path-specific effects Tech. Rep. R-321, Dep. Stat., Univ. Calif. Los Angeles https://escholarship.org/uc/item/45x689gq
    [Google Scholar]
  24. Bang H, Robins JM. 2005. Doubly robust estimation in missing data and causal inference models. Biometrics 61:962–73
    [Google Scholar]
  25. Baron RM, Kenny DA. 1986. The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51:1173–82
    [Google Scholar]
  26. Basse GW, Airoldi EM. 2018. Limitations of design-based causal inference and A/B testing under arbitrary and network interference. Sociol. Methodol. 48:136–51
    [Google Scholar]
  27. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
    [Google Scholar]
  28. Bertanha M, Imbens GW. 2020. External validity in fuzzy regression discontinuity designs. J. Bus. Econ. Stat. 38:593–612
    [Google Scholar]
  29. Blandhol C, Bonney J, Mogstad M, Torgovitsky A. 2022. When is TSLS actually LATE? NBER Work. Pap. w29709
    [Google Scholar]
  30. Bloome D, Schrage D. 2021. Covariance regression models for studying treatment effect heterogeneity across one or more outcomes: understanding how treatments shape inequality. Sociol. Methods Res. 50:1034–72
    [Google Scholar]
  31. Bollen KA. 2014. Structural Equations with Latent Variables New York: John Wiley & Sons
    [Google Scholar]
  32. Bound J, Jaeger DA, Baker RM. 1995. Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J. Am. Stat. Assoc. 90:443–50
    [Google Scholar]
  33. Brand JE. 2023. Overcoming the Odds: The Benefits for Unlikely College Graduates New York: Russell Sage Found.
    [Google Scholar]
  34. Brand JE, Moore R, Song X, Xie Y. 2019a. Parental divorce is not uniformly disruptive to children's educational attainment. PNAS 116:7266–71
    [Google Scholar]
  35. Brand JE, Moore R, Song X, Xie Y. 2019b. Why does parental divorce lower children's educational attainment? A causal mediation analysis. Sociol. Sci. 6:264–92
    [Google Scholar]
  36. Brand JE, Simon Thomas J 2013. Causal effect heterogeneity. In Handbook of Causal Analysis for Social Research SL Morgan 189–214. New York: Springer
    [Google Scholar]
  37. Brand JE, Simon Thomas J 2014. Job displacement among single mothers: effects on children's outcomes in young adulthood. Am. J. Sociol. 119:955–1001
    [Google Scholar]
  38. Brand JE, Xie Y. 2007. Identification and estimation of causal effects with time-varying treatments and time-varying outcomes. Sociol. Methodol. 37:393–434
    [Google Scholar]
  39. Brand JE, Xie Y. 2010. Who benefits most from college? Evidence for negative selection in heterogeneous economic returns to higher education. Am. Sociol. Rev. 75:273–302
    [Google Scholar]
  40. Brand JE, Xu J, Koch B 2020. Machine learning. Research Methods in the Social Sciences Foundation P Atkinson, S Delamont, A Cernat, JW Sakshaug, RA Williams 1–27. Thousand Oaks, CA: SAGE
    [Google Scholar]
  41. Brand JE, Xu J, Koch B, Geraldo P. 2021. Uncovering sociological effect heterogeneity using machine-learning. Sociol. Methodol. 51:189–223
    [Google Scholar]
  42. Breiman L. 2001. Random forests. Int. J. Mach. Learn. Cybern. 45:5–32
    [Google Scholar]
  43. Caliendo M, Kopeinig S. 2008. Some practical guidance for the implementation of propensity score matching. J. Econ. Surv. 22:31–72
    [Google Scholar]
  44. Card D. 2001. Estimating the return to schooling: progress on some persistent econometric problems. Econometrica 69:1127–60
    [Google Scholar]
  45. Carranza AG, Krishnamurthy SK, Athey S. 2022. Flexible and efficient contextual bandits with heterogeneous treatment effect oracles. arXiv:2203.16668 [cs.LG]
  46. Cattaneo MD, Idrobo N, Titiunik R. 2019. A Practical Introduction to Regression Discontinuity Designs: Foundations Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  47. Cattaneo MD, Titiunik R. 2022. Regression discontinuity designs. Annu. Rev. Econ. 14:821–51
    [Google Scholar]
  48. Cheng S, Brand JE, Zhou X, Xie Y, Hout M. 2021. Heterogeneous returns to college over the life course. Sci. Adv. 7:eabg7641
    [Google Scholar]
  49. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W, Robins J. 2018. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
    [Google Scholar]
  50. Cinelli C, Forney A, Pearl J. 2022. A crash course in good and bad controls. Sociol. Methods Res. In press. https://doi.org/10.1177/00491241221099552
    [Google Scholar]
  51. Cinelli C, Hazlett C. 2020. Making sense of sensitivity: extending omitted variable bias. J. R. Stat. Soc. Ser. B 82:39–67
    [Google Scholar]
  52. Cole SR, Stuart EA. 2010. Generalizing evidence from randomized clinical trials to target populations: the ACTG-320 trial. Am. J. Epidemiol. 172:107–15
    [Google Scholar]
  53. Daniel RM, De Stavola BL, Cousens SN, Vansteelandt S. 2015. Causal mediation analysis with multiple mediators. Biometrics 71:11–14
    [Google Scholar]
  54. Deaton A. 2010. Instruments, randomization, and learning about development. J. Econ. Lit. 48:424–55
    [Google Scholar]
  55. Deaton A, Cartwright N. 2018. Understanding and misunderstanding randomized controlled trials. Soc. Sci. Med. 210:2–21
    [Google Scholar]
  56. Díaz I, Hejazi NS, Rudolph KE, van Der Laan MJ. 2021. Nonparametric efficient causal mediation with intermediate confounders. Biometrika 108:627–41
    [Google Scholar]
  57. Didelez V, Dawid AP, Geneletti S 2006. Direct and indirect effects of sequential treatments. UAI'06: Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence R Dechter, T Richardson 138–46. Arlington, VA: AUAI
    [Google Scholar]
  58. Dong Y, Lewbel A. 2015. Identifying the effect of changing the policy threshold in regression discontinuity models. Rev. Econ. Stat. 97:1081–1092
    [Google Scholar]
  59. Duncan OD. 1966. Path analysis: sociological examples. Am. J. Sociol. 72:1–16
    [Google Scholar]
  60. Egami N. 2021. Spillover effects in the presence of unobserved networks. Political Anal. 29:3287–316
    [Google Scholar]
  61. Egami N, Hartman E. 2022. Elements of external validity: framework, design, and analysis. Am. Political Sci. Rev. In press. https://doi.org/10.1017/S0003055422000880
    [Google Scholar]
  62. Elwert F. 2015. Graphical causal models. Handbook of Causal Analysis for Social Research SL Morgan 245–73. New York: Springer
    [Google Scholar]
  63. Elwert F, Pfeffer FT. 2019. The future strikes back: using future treatments to detect and reduce hidden bias. Sociol. Methods Res. 51:1014–51
    [Google Scholar]
  64. Elwert F, Winship C. 2014. Endogenous selection bias: the problem of conditioning on a collider variable. Annu. Rev. Sociol. 40:31–53
    [Google Scholar]
  65. Felton C, Stewart B. 2022. Handle with care: a sociologist's guide to causal inference with instrumental variables. SocArXiv. https://doi.org/10.31235/osf.io/3ua7q
  66. Findley MG, Kikuta K, Denley M. 2021. External validity. Annu. Rev. Political Sci. 24:365–93
    [Google Scholar]
  67. Fisher RA. 1935.. The Design of Experiments Edinburgh: Oliver & Boyd
    [Google Scholar]
  68. Fong C, Hazlett C, Imai K. 2018. Covariate balancing propensity score for a continuous treatment: application to the efficacy of political advertisements. Ann. Appl. Stat. 12:156–77
    [Google Scholar]
  69. Gangl M. 2010. Causal inference in sociological research. Annu. Rev. Sociol. 36:21–47
    [Google Scholar]
  70. Gangl M. 2015. Partial identification and sensitivity analysis. Handbook of Causal Analysis for Social Research SL Morgan 377–402. New York: Springer
    [Google Scholar]
  71. Geneletti S. 2007. Identifying direct and indirect effects in a non-counterfactual framework. J. R. Stat. Soc. Ser. B 69:199–215
    [Google Scholar]
  72. Gill RD, Robins JM. 2001. Causal inference for complex longitudinal data: the continuous case. Ann. Stat. 29:1785–811
    [Google Scholar]
  73. Grimmer J, Roberts ME, Stewart BE. 2021. Machine learning for social science: an agnostic approach. Annu. Rev. Political Sci. 24:395–419
    [Google Scholar]
  74. Hahn J, Todd P, Van der Klaauw W. 2001. Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica 69:201–9
    [Google Scholar]
  75. Hahn PR, Murray J, Carvalho C. 2020. Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects. Bayesian Anal. 15:965–1056
    [Google Scholar]
  76. Hainmueller J. 2012. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Political Anal. 20:25–46
    [Google Scholar]
  77. Hartman E, Grieve R, Ramsahai R, Sekhon JS. 2015. From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects. J. R. Stat. Soc. Ser. A 178:757–78
    [Google Scholar]
  78. Hastie T, Tibshirani R, Friedman JH. 2017. The Elements of Statistical Learning Berlin: Springer. , 2nd ed..
    [Google Scholar]
  79. Heckman JJ, Humphries JE, Veramendi G. 2018. The nonmarket benefits of education and ability. J. Hum. Cap. 12:282–304
    [Google Scholar]
  80. Heckman JJ, Robb R. 1986. Alternative methods for solving the problem of selection bias in evaluating the impact of treatments on outcomes. Drawing Inferences from Self-Selected Samples H Wainer 63–107. Mahwah, NJ: Lawrence Erlbaum
    [Google Scholar]
  81. Heckman JJ, Urzua S, Vytlacil E. 2006. Understanding instrumental variables in models with essential heterogeneity. Rev. Econ. Stat. 88:389–432
    [Google Scholar]
  82. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
    [Google Scholar]
  83. Hong G, Raudenbush S. 2015. Heterogeneous agents, social interactions, and causal inference. Handbook of Causal Analysis for Social Research SL Morgan 331–52. New York: Springer
    [Google Scholar]
  84. Hotz JV, Imbens GW, Mortimer JH. 2005. Predicting the efficacy of future training programs using past experiences at other locations. J. Econom. 125:241–70
    [Google Scholar]
  85. Huber M. 2023. Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R. Cambridge, MA: MIT Press
    [Google Scholar]
  86. Hudgens MG, Halloran ME. 2008. Toward causal inference with interference. J. Am. Stat. Assoc. 103:832–42
    [Google Scholar]
  87. Imai K, Jiang Z. 2020. Identification and sensitivity analysis of contagion effects in randomized placebo-controlled trials. J. R. Stat. Soc. Ser. A 183:1637–57
    [Google Scholar]
  88. Imai K, Keele L, Yamamoto T. 2010. Identification, inference and sensitivity analysis for causal mediation effects. Stat. Sci. 25:51–71
    [Google Scholar]
  89. Imai K, Kim IS. 2019. When should we use unit fixed effects regression models for causal inference with longitudinal data?. Am. J. Political Sci. 63:467–90
    [Google Scholar]
  90. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
    [Google Scholar]
  91. Imai K, Ratkovic M. 2014. Covariate balancing propensity score. J. R. Stat. Soc. Ser. B 76:243–63
    [Google Scholar]
  92. Imai K, Yamamoto T. 2013. Identification and sensitivity analysis for multiple causal mechanisms: revisiting evidence from framing experiments. Political Anal. 21:141–71
    [Google Scholar]
  93. Imbens GW. 2004. Nonparametric estimation of average treatment effects under exogeneity: a review. Rev. Econ. Stat. 86:4–29
    [Google Scholar]
  94. Imbens GW. 2015. Matching methods in practice. J. Hum. Resour. 50:373–419
    [Google Scholar]
  95. Imbens GW, Angrist JD. 1994. Identification and estimation of local average treatment effects. Econometrica 62:467–75
    [Google Scholar]
  96. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
    [Google Scholar]
  97. Imbens GW, Rubin D. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  98. Josey KP, Yang F, Ghosh D, Raghavan S. 2022. A calibration approach to transportability and data-fusion with observational data. Stat. Med. 41:4511–31
    [Google Scholar]
  99. Kennedy EH, Ma Z, McHugh MD, Small DS. 2017. Non-parametric methods for doubly robust estimation of continuous treatment effects. J. R. Stat. Soc. Ser. B 79:1229–45
    [Google Scholar]
  100. Kern HL, Stuart EA, Hill J, Green DP. 2016. Assessing methods for generalizing experimental impact estimates to target populations. J. Res. Educ. Eff. 9:103–27
    [Google Scholar]
  101. Klein M, Kühhirt M. 2021. Direct and indirect effects of grandparent education on grandchildren's cognitive development: the role of parental cognitive ability. Sociol. Sci. 8:265–84
    [Google Scholar]
  102. Künzel SR, Sekhon JS, Bickel PJ, Yu B. 2019. Metalearners for estimating heterogeneous treatment effects using machine learning. PNAS 116:4156–65
    [Google Scholar]
  103. Lee BK, Lessler J, Stuart E. 2010. Improving propensity score weighting using machine learning. Stat. Med. 29:337–46
    [Google Scholar]
  104. Lee D, McLanahan S. 2015. Family structure transitions and child development: instability, selection, and population heterogeneity. Am. Sociol. Rev. 80:738–63
    [Google Scholar]
  105. Lee DS, Lemieux T. 2010. Regression discontinuity designs in economics. J. Econ. Lit. 48:281–355
    [Google Scholar]
  106. Lee Y, Ogburn EL. 2021. Network dependence can lead to spurious associations and invalid inference. J. Am. Stat. Assoc. 116:1060–74
    [Google Scholar]
  107. Levy BL, Owens A, Sampson RJ. 2019. The varying effects of neighborhood disadvantage on college graduation: moderating and mediating mechanisms. Sociol. Educ. 92:269–92
    [Google Scholar]
  108. Liu L, Hudgens MG, Saul B, Clemens JD, Ali M, Emch ME. 2019. Doubly robust estimation in observational studies with partial interference. Stat 8:e214
    [Google Scholar]
  109. Lundberg I. 2022. The gap-closing estimand: a causal approach to study interventions that close disparities across social categories. Sociol. Methods Res In press. https://doi.org/10.1177/00491241211055769
    [Google Scholar]
  110. Lundberg I, Brand JE, Jeon N. 2022. Researcher reasoning meets computational capacity: machine learning for social science. Soc. Sci. Res. 108:102807
    [Google Scholar]
  111. Lundberg I, Johnson R, Stewart BM. 2021. What is your estimand? Defining the target quantity connects statistical evidence to theory. Am. Sociol. Rev. 86:532–65
    [Google Scholar]
  112. Manski CF. 1995. Identification Problems in the Social Sciences Cambridge, MA: Harvard Univ. Press
    [Google Scholar]
  113. Manski CF, Garfinkel I 1992. Introduction. Evaluating Welfare and Training Programs CF Manski, I Garfinkel 1–21. Cambridge, MA: Harvard Univ. Press
    [Google Scholar]
  114. Mason WM, Wong GY, Entwisle B. 1983. Contextual analysis through the multilevel linear model. Sociol. Methodol. 14:72–103
    [Google Scholar]
  115. McCaffrey DF, Ridgeway G, Morral AR. 2004. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol. Methods 9:403
    [Google Scholar]
  116. Miles CH, Shpitser I, Kanki P, Meloni S, Tchetgen Tchetgen EJ 2017. Quantifying an adherence path-specific effect of antiretroviral therapy in the Nigeria PEPFAR program. J. Am. Stat. Assoc. 112:1443–52
    [Google Scholar]
  117. Miles CH, Shpitser I, Kanki P, Meloni S, Tchetgen Tchetgen EJ 2020. On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding. Biometrika 107:159–72
    [Google Scholar]
  118. Mogstad M, Torgovitsky A. 2018. Identification and extrapolation of causal effects with instrumental variables. Annu. Rev. Econ. 10:577–613
    [Google Scholar]
  119. Molina M, Garip F. 2019. Machine learning for sociology. Annu. Rev. Sociol. 45:27–45
    [Google Scholar]
  120. Morgan S, Harding D. 2006. Matching estimators of causal effects: prospects and pitfalls in theory and practice. Sociol. Methods Res. 35:3–60
    [Google Scholar]
  121. Morgan S, Winship C. 2014. Counterfactuals and Causal Inference: Methods and Principles for Social Research Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  122. Naimi AI, Cole SR, Kennedy EH. 2017. An introduction to g methods. Int. J. Epidemiol. 46:756–62
    [Google Scholar]
  123. Neyman J. 1923. On the application of probability theory to agricultural experiments. Stat. Sci. 5:465–80
    [Google Scholar]
  124. Nguyen TQ, Schmid I, Stuart EA. 2021. Clarifying causal mediation analysis for the applied researcher: defining effects based on what we want to learn. Psychol. Methods 26:255–71
    [Google Scholar]
  125. Nie X, Wager S. 2021. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika 108:299–319
    [Google Scholar]
  126. Offer-Westort M, Coppock A, Green DP. 2021. Adaptive experimental design: prospects and applications in political science. Am. J. Political Sci. 65:826–44
    [Google Scholar]
  127. Ogburn EL, Sofrygin O, Diaz I, Van Der Laan MJ. 2022. Causal inference for social network data. J. Am. Stat. Assoc. In press. https://doi.org/10.1080/01621459.2022.2131557
    [Google Scholar]
  128. Park C, Kang H. 2022. Efficient semiparametric estimation of network treatment effects under partial interference. Biometrika 109:1015–31
    [Google Scholar]
  129. Pearl J. 2001. Direct and indirect effects. Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence J Breese, D Koller 411–20. Burlington, MA: Morgan Kaufmann
    [Google Scholar]
  130. Pearl J. 2009.. Causality: Models, Reasoning, and Inference Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  131. Pearl J, Bareinboim E. 2014. External validity: from do-calculus to transportability across populations. Stat. Sci. 29:579–95
    [Google Scholar]
  132. Quandt R. 1972. A new approach to estimating switching regression. J. Am. Stat. Assoc. 67:306–10
    [Google Scholar]
  133. Reardon SF, Raudenbush SW. 2013. Under what assumptions do site-by-treatment instruments identify average causal effects?. Sociol. Methods Res. 42:143–63
    [Google Scholar]
  134. Robins JM. 1986. A new approach to causal inference in mortality studies with a sustained exposure period-application to control of the healthy worker survivor effect. Math. Model. 7:1393–512
    [Google Scholar]
  135. Robins JM. 1997. Causal inference from complex longitudinal data. Latent Variable Modeling and Applications to Causality M Berkane 69–117. New York: Springer
    [Google Scholar]
  136. Robins JM 2003. Semantics of causal DAG models and the identification of direct and indirect effects. Highly Structured Stochastic Systems PJ Green, NL Hjort, S Richardson 70–81. Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  137. Robins JM, Greenland S. 1992. Identifiability and exchangeability for direct and indirect effects. Epidemiology 3:143–55
    [Google Scholar]
  138. Robins JM, Hernan MA, Brumback B. 2000. Marginal structural models and causal inference in epidemiology. Epidemiology 11:550–60
    [Google Scholar]
  139. Robins JM, Richardson TS, Shpitser I 2022. An interventionist approach to mediation analysis. Probabilistic and Causal Inference: The Works of Judea Pearl H Geffner, R Dechter, JY Halpern 713–64. New York: ACM
    [Google Scholar]
  140. Robins JM, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
    [Google Scholar]
  141. Robins JM, Rotnitzky A, Zhao LP. 1994. Estimation of regression coefficients when some regressors are not always observed. J. Am. Stat. Assoc. 89:846–66
    [Google Scholar]
  142. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
    [Google Scholar]
  143. Rotnitzky A, Robins JM, Babino L. 2017. On the multiply robust estimation of the mean of the g-functional. arXiv:1705.08582 [stat.ME]
  144. Roy AD. 1951. Some thoughts on the distribution of earnings. Oxf. Econ. Pap. 3:135–46
    [Google Scholar]
  145. Rubin DB. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66:688–701
    [Google Scholar]
  146. Rubin DB. 1977. Assignment to treatment group on the basis of a covariate. J. Educ. Stat. 2:1–26
    [Google Scholar]
  147. Rubin DB. 1986. Which ifs have causal answers? Discussion of “Statistics and Causal Inference” by Holland. J. Am. Stat. Assoc. 83:396
    [Google Scholar]
  148. Scharfstein DO, Rotnitzky A, Robins JM. 1999. Adjusting for nonignorable drop-out using semiparametric nonresponse models. J. Am. Stat. Assoc. 94:1096–120
    [Google Scholar]
  149. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
    [Google Scholar]
  150. Semenova V, Chernozhukov V. 2021. Debiased machine learning of conditional average treatment effects and other causal functions. Econom. J. 24:264–89
    [Google Scholar]
  151. Shu X, Ye Y. 2023. Knowledge discovery: methods from data mining and machine learning. Soc. Sci. Res. 110:102817
    [Google Scholar]
  152. Sobel ME. 2006. What do randomized studies of housing mobility demonstrate? Causal inference in the face of interference. J. Am. Stat. Assoc. 101:1398–407
    [Google Scholar]
  153. Steiner PM, Kim Y, Hall CE, Su D. 2017. Graphical models for quasi-experimental designs. Sociol. Methods Res. 46:155–88
    [Google Scholar]
  154. Stuart EA, Bradshaw CP, Leaf PJ. 2015. Assessing the generalizability of randomized trial results to target populations. Prev. Sci. 16:475–85
    [Google Scholar]
  155. Stuart EA, Cole SR, Bradshaw CP, Leaf PJ. 2011. The use of propensity scores to assess the generalizability of results from randomized trials. J. R. Stat. Soc. Ser. A 174:369–86
    [Google Scholar]
  156. Tchetgen Tchetgen EJ, Shpitser I. 2012. Semiparametric theory for causal mediation analysis: efficiency bounds, multiple robustness, and sensitivity analysis. Ann. Stat. 40:1816–45
    [Google Scholar]
  157. Tchetgen Tchetgen EJ, VanderWeele TJ. 2012. On causal inference in the presence of interference. Stat. Methods Med. Res. 21:55–75
    [Google Scholar]
  158. Tipton E. 2013. Improving generalizations from experiments using propensity score subclassification: assumptions, properties, and contexts. J. Educ. Behav. Stat. 38:239–66
    [Google Scholar]
  159. Tipton E, Hedges L, Vaden-Kiernan M, Borman G, Sullivan K, Caverly S. 2014. Sample selection in randomized experiments: a new method using propensity score stratified sampling. J. Res. Educ. Eff. 7:114–35
    [Google Scholar]
  160. van der Laan MJ, Rose S. 2018. Targeted Learning in Data Science New York: Springer
    [Google Scholar]
  161. van der Laan MJ, Rubin D. 2006. Targeted maximum likelihood learning. Int. J. Biostat. 2:11
    [Google Scholar]
  162. VanderWeele TJ. 2009. Marginal structural models for the estimation of direct and indirect effects. Epidemiology 20:18–26
    [Google Scholar]
  163. VanderWeele TJ. 2010. Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology 21:540–51
    [Google Scholar]
  164. VanderWeele TJ. 2015. Explanation in Causal Inference: Methods for Mediation and Interaction Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  165. VanderWeele TJ. 2016. Mediation analysis: a practitioner's guide. Annu. Rev. Public Health 37:17–32
    [Google Scholar]
  166. VanderWeele TJ, Arah OA. 2011. Unmeasured confounding for general outcomes, treatments, and confounders: bias formulas for sensitivity analysis. Epidemiology 22:42–52
    [Google Scholar]
  167. VanderWeele TJ, Vansteelandt S. 2009. Conceptual issues concerning mediation, interventions and composition. Stat. Interface 2:457–68
    [Google Scholar]
  168. VanderWeele TJ, Vansteelandt S, Robins JM. 2014. Effect decomposition in the presence of an exposure-induced mediator-outcome confounder. Epidemiology 25:300–6
    [Google Scholar]
  169. Vansteelandt S. 2009. Estimating direct effects in cohort and case–control studies. Epidemiology 20:851–60
    [Google Scholar]
  170. Vansteelandt S, Bekaert M, Lange T. 2012. Imputation strategies for the estimation of natural direct and indirect effects. Epidemiol. Methods 1:131–158
    [Google Scholar]
  171. Vansteelandt S, Sjolander A. 2016. Revisiting g-estimation of the effect of a time-varying exposure subject to time-varying confounding. Epidemiol. Methods 5:37–56
    [Google Scholar]
  172. Wager S, Athey S. 2018. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
    [Google Scholar]
  173. Westreich D, Edwards JK, Lesko CR, Cole SR, Stuart EA. 2019. Target validity and the hierarchy of study designs. Am. J. Epidemiol. 188:438–43
    [Google Scholar]
  174. Westreich D, Lessler J, Funk MJ. 2010. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J. Clin. Epidemiol. 63:826–33
    [Google Scholar]
  175. Winship C, Morgan SL. 1999. The estimation of causal effects from observational data. Annu. Rev. Sociol. 25:659–707
    [Google Scholar]
  176. Wodtke GT. 2020. Regression-based adjustment for time-varying confounders. Sociol. Methods Res. 49:906–46
    [Google Scholar]
  177. Wodtke GT, Alaca Z, Zhou X. 2020a. Regression-with-residuals estimation of marginal effects: a method of adjusting for treatment-induced confounders that may also be effect modifiers. J. R. Stat. Soc. Ser. A 183:311–32
    [Google Scholar]
  178. Wodtke GT, Almirall D. 2017. Estimating moderated causal effects with time-varying treatments and time-varying moderators: structural nested mean models and regression with residuals. Sociol. Methodol. 47:212–45
    [Google Scholar]
  179. Wodtke GT, Harding DJ, Elwert F. 2011. Neighborhood effects in temporal perspective: the impact of long-term exposure to concentrated disadvantage on high school graduation. Am. Sociol. Rev. 76:713–36
    [Google Scholar]
  180. Wodtke GT, Parbst M. 2017. Neighborhoods, schools, and academic achievement: a formal mediation analysis of contextual effects on reading and mathematics abilities. Demography 54:1653–76
    [Google Scholar]
  181. Wodtke GT, Ramaj S, Schachner J. 2022. Toxic neighborhoods: the effects of concentrated poverty and environmental lead contamination on early childhood development. Demography 59:1275–98
    [Google Scholar]
  182. Wodtke GT, Yildirim U, Harding DJ, Elwert F 2020b. Are neighborhood effects explained by differences in school quality? Work. Pap. 102-20 Inst. Res. Labor Employ., Univ. Calif. Berkeley, CA:
    [Google Scholar]
  183. Wodtke GT, Zhou X. 2020. Effect decomposition in the presence of treatment-induced confounding: a regression-with-residuals approach. Epidemiology 31:369–75
    [Google Scholar]
  184. Xia F, Chan KCG. 2021. Identification, semiparametric efficiency, and quadruply robust estimation in mediation analysis with treatment-induced confounding. J. Am. Stat. Assoc. https://doi.org/10.1080/01621459.2021.1990765
    [Crossref] [Google Scholar]
  185. Xie Y. 2013. Population heterogeneity and causal inference. PNAS 110:6262–68
    [Google Scholar]
  186. Xie Y, Brand JE, Jann B 2012. Estimating heterogeneous treatment effects with observational data. Sociol. Methodol. 42:314–47
    [Google Scholar]
  187. Xie Y, Near C, Xu H, Song X. 2020. Heterogeneous treatment effects on children's cognitive/non-cognitive skills: a reevaluation of an influential early childhood intervention. Soc. Sci. Res. 86:102389
    [Google Scholar]
  188. Yadlowsky S, Fleming S, Shah N, Brunskill E, Wager S. 2021. Evaluating treatment prioritization rules via rank-weighted average treatment effects. arXiv:2111.07966 [stat.ME]
  189. Zhou X. 2019. Equalization or selection? Reassessing the “meritocratic power” of a college degree in intergenerational income mobility. Am. Sociol. Rev. 84:459–85
    [Google Scholar]
  190. Zhou X. 2022a. Attendance, completion, and heterogeneous returns to college: a causal mediation approach. Sociol. Methods Res. In press. https://doi.org/10.1177/00491241221113876
    [Google Scholar]
  191. Zhou X. 2022b. Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators. J. R. Stat. Soc. Ser. B 84:794–821
    [Google Scholar]
  192. Zhou X, Pan G. 2023. Higher education and the black-white earnings gap. Am. Sociol. 88:1154–88
    [Google Scholar]
  193. Zhou X, Wodtke GT 2019. A regression-with-residuals method for estimating controlled direct effects. Political Anal. 27:360–69
    [Google Scholar]
  194. Zhou X, Wodtke GT. 2020. Residual balancing: a method of constructing weights for marginal structural models. Political Anal. 28:487–506
    [Google Scholar]
  195. Zhou X, Xie Y. 2019. Marginal treatment effects from a propensity score perspective. J. Political Econ. 127:3070–84
    [Google Scholar]
  196. Zhou X, Xie Y. 2020. Heterogeneous treatment effects in the presence of self-selection: a propensity score perspective. Sociol. Methodol. 50:350–85
    [Google Scholar]
  197. Zhou X, Yamamoto T. 2022. Tracing causal paths from experimental and observational data. J. Politics 85:1250–65
    [Google Scholar]
  198. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
    [Google Scholar]
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