Direct and Indirect Effects based on Changes-in-Changes

References

• Albert, J. M., and Nelson, S. (2011), “Generalized Causal Mediation Analysis,” Biometrics, 67, 1028–1038. DOI: https://doi.org/10.1111/j.1541-0420.2010.01547.x.
   PubMed Web of Science ®Google Scholar
• Angrist, J., and Evans, W. (1998), “Children and Their Parents Labor Supply: Evidence From Exogeneous Variation in Family Size,” American Economic Review, 88, 450–477.
   Web of Science ®Google Scholar
• Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996), “Identification of Causal Effects Using Instrumental Variables,” Journal of the American Statistical Association, 91, 444–472.
   Web of Science ®Google Scholar
• Athey, S., and Imbens, G. W. (2006), “Identification and Inference in Nonlinear Difference-in-Difference Models,” Econometrica, 74, 431–497.
   Web of Science ®Google Scholar
• Balke, A., and Pearl, J. (1997), “Bounds on Treatment Effects From Studies With Imperfect Compliance,” Journal of the American Statistical Association, 92, 1171–1176.
   Web of Science ®Google Scholar
• Baron, R. M., and Kenny, D. A. (1986), “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations,” Journal of Personality and Social Psychology, 51, 1173–1182.
   PubMed Web of Science ®Google Scholar
• Bellani, L., and Bia, M. (2018), “The Long-Run Effect of Childhood Poverty and the Mediating Role of Education,” Journal of the Royal Statistical Society, Series A, 182, 37–68.
   Web of Science ®Google Scholar
• Bijwaard, G. E., and Jones, A. M. (2019), “An IPW Estimator for Mediation Effects in Hazard Models: With an Application to Schooling, Cognitive Ability and Mortality,” Empirical Economics, 57, 129–175.
   Web of Science ®Google Scholar
• Brunello, G., Fort, M., Schneeweis, N., and Winter-Ebmer, R. (2016), “The Causal Effect of Education on Health: What Is the Role of Health Behaviors?,” Health Economics, 25, 314–336.
   PubMed Web of Science ®Google Scholar
• Chaisemartin, C., and D’Haultfeuille, X. (2018), “Fuzzy Differences-in-Differences,” Review of Economic Studies, 85, 999–1028.
   Web of Science ®Google Scholar
• Chen, S. H., Chen, Y. C., and Liu, J. T. (2019), “The Impact of Family Composition on Educational Achievement,” Journal of Human Resources, 54, 122–170.
   Web of Science ®Google Scholar
• Chen, X., Flores, C., and Flores-Lagunes, A. (2016), “Bounds on Average Treatment Effects With an Invalid Instrument: An Application to the Oregon Health Insurance Experiment,” Working Paper.
   Google Scholar
• Cochran, W. G. (1957), “Analysis of Covariance: Its Nature and Uses,” Biometrics, 13, 261–281.
   Web of Science ®Google Scholar
• Conti, G., Heckman, J. J., and Pinto, R. (2016), “The Effects of Two Influential Early Childhood Interventions on Health and Healthy Behaviour,” Economic Journal, 126, F28–F65. DOI: https://doi.org/10.1111/ecoj.12420.
   PubMed Web of Science ®Google Scholar
• Deuchert, E., Huber, M., and Schelker, M. (2019), “Direct and Indirect Effects Based on Difference-in-Differences With an Application to Political Preferences Following the Vietnam Draft Lottery,” Journal of Business & Economic Statistics, 37, 710–720.
   Web of Science ®Google Scholar
• Doerr, A., and Strittmatter, A. (2019), “Identifying Causal Channels of Policy Reforms With Multiple Treatments and Different Types of Selection,” Working Paper.
   Google Scholar
• Farbmacher, H., and Guber, R. (2018), “Instrument Validity Tests With Causal Trees,” Working Paper.
   Google Scholar
• Flores, C. A., and Flores-Lagunes, A. (2009), “Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment Under Unconfoundedness,” IZA Discussion Paper No. 4237.
   Google Scholar
• Flores, C. A., and Flores-Lagunes, A. (2010), “Nonparametric Partial Identification of Causal Net and Mechanism Average Treatment Effects,” Mimeo, University of Florida.
   Google Scholar
• Flores, C. A., and Flores-Lagunes, A. (2013), “Partial Identification of Local Average Treatment Effects With an Invalid Instrument,” Journal of Business & Economic Statistics, 31, 534–545.
   Web of Science ®Google Scholar
• Frangakis, C., and Rubin, D. (2002), “Principal Stratification in Causal Inference,” Biometrics, 58, 21–29. DOI: https://doi.org/10.1111/j.0006-341x.2002.00021.x.
   PubMed Web of Science ®Google Scholar
• Frölich, M., and Huber, M. (2017), “Direct and Indirect Treatment Effects—Causal Chains and Mediation Analysis With Instrumental Variables,” Journal of the Royal Statistical Society, Series B, 79, 1645–1666.
   Google Scholar
• Frölich, M., and Melly, B. (2013), “Identification of Treatment Effects on the Treated With One-Sided Non-Compliance,” Econometric Reviews, 32, 384–414.
   Web of Science ®Google Scholar
• Hausman, J. A. (1978), “Specification Tests in Econometrics,” Econometrica, 46, 1251–1271.
   Web of Science ®Google Scholar
• Heckman, J., Pinto, R., and Savelyev, P. (2013), “Understanding the Mechanisms Through Which an Influential Early Childhood Program Boosted Adult Outcomes,” American Economic Review, 103, 2052–2086. DOI: https://doi.org/10.1257/aer.103.6.2052.
   PubMed Web of Science ®Google Scholar
• Heckman, J. J., and Vytlacil, E. (2005), “Structural Equations, Treatment Effects, and Econometric Policy Evaluation,” Econometrica, 73, 669–738.
   Web of Science ®Google Scholar
• Hong, G. (2010), “Ratio of Mediator Probability Weighting for Estimating Natural Direct and Indirect Effects,” in Proceedings of the American Statistical Association, Biometrics Section, Alexandria, VA: American Statistical Association, pp. 2401–2415.
   Google Scholar
• Huber, M. (2014), “Identifying Causal Mechanisms (Primarily) Based on Inverse Probability Weighting,” Journal of Applied Econometrics, 29, 920–943.
   Web of Science ®Google Scholar
• Huber, M. (2015), “Causal Pitfalls in the Decomposition of Wage Gaps,” Journal of Business & Economic Statistics, 33, 179–191.
   Web of Science ®Google Scholar
• Huber, M., Lechner, M., and Mellace, G. (2017), “Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism,” Review of Economics and Statistics, 99, 180–183.
   Web of Science ®Google Scholar
• Huber, M., Lechner, M., and Strittmatter, A. (2018), “Direct and Indirect Effects of Training Vouchers for the Unemployed,” Journal of the Royal Statistical Society, Series A, 181, 441–463.
   Web of Science ®Google Scholar
• Huber, M., and Mellace, G. (2015), “Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints,” Review of Economics and Statistics, 97, 398–411.
   Web of Science ®Google Scholar
• Imai, K., Keele, L., and Yamamoto, T. (2010), “Identification, Inference and Sensitivity Analysis for Causal Mediation Effects,” Statistical Science, 25, 51–71.
   Web of Science ®Google Scholar
• Imai, K., and Yamamoto, T. (2013), “Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence From Framing Experiments,” Political Analysis, 21, 141–171.
   Web of Science ®Google Scholar
• Imbens, G. W., and Angrist, J. (1994), “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 62, 467–475.
   Web of Science ®Google Scholar
• Judd, C. M., and Kenny, D. A. (1981), “Process Analysis: Estimating Mediation in Treatment Evaluations,” Evaluation Review, 5, 602–619.
   Web of Science ®Google Scholar
• Juhn, C., Murphy, K. M., and Pierce, B. (1991), “Accounting for the Slowdown in Black-White Wage Convergence,” in Workers and Their Wages: Changing Patterns in the United States, ed. M. H. Kosters, Washington, DC: American Enterprise Institute Press, pp. 107–143.
   Google Scholar
• Keele, L., Tingley, D., and Yamamoto, T. (2015), “Identifying Mechanisms Behind Policy Interventions via Causal Mediation Analysis,” Journal of Policy Analysis and Management, 34, 937–963.
   Web of Science ®Google Scholar
• Kitagawa, T. (2015), “A Test for Instrument Validity,” Econometrica, 83, 2043–2063.
   Web of Science ®Google Scholar
• Lechner, M., and Strittmatter, A. (2019), “Practical Procedures to Deal With Common Support Problems in Matching Estimation,” Econometric Reviews, 38, 193–207.
   Web of Science ®Google Scholar
• Lee, J. (2008), “Sibling Size and Investment in Children’s Education: An Asian Instrument,” Journal of Population Economics, 21, 855–875.
   Web of Science ®Google Scholar
• Manski, C. F. (1988), Analog Estimation Methods in Econometrics, New York: Chapman & Hall.
   Google Scholar
• Melly, B., and Santangelo, G. (2015), “The Changes-in-Changes Model With Covariates,” Working Paper.
   Google Scholar
• Mourifié, I., and Wan, Y. (2017), “Testing LATE Assumptions,” Review of Economics and Statistics, 99, 305–313.
   Web of Science ®Google Scholar
• Pearl, J. (2001), “Direct and Indirect Effects,” in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, San Francisco, CA: Morgan Kaufman, pp. 411–420.
   Google Scholar
• Petersen, M. L., Sinisi, S. E., and van der Laan, M. J. (2006), “Estimation of Direct Causal Effects,” Epidemiology, 17, 276–284.
   PubMed Web of Science ®Google Scholar
• Powdthavee, N., Lekfuangfu, W. N., and Wooden, M. (2013), “The Marginal Income Effect of Education on Happiness: Estimating the Direct and Indirect Effects of Compulsory Schooling on Well-Being in Australia,” IZA Discussion Paper No. 7365.
   Google Scholar
• Robins, J. M. (2003), “Semantics of Causal DAG Models and the Identification of Direct and Indirect Effects,” in Highly Structured Stochastic Systems, eds. P. Green, N. Hjort, and S. Richardson, Oxford: Oxford University Press, pp. 70–81.
   Google Scholar
• Robins, J. M., and Greenland, S. (1992), “Identifiability and Exchangeability for Direct and Indirect Effects,” Epidemiology, 3, 143–155.
   PubMed Web of Science ®Google Scholar
• Rosenbaum, P. R., and Rubin, D. B. (1985), “Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score,” American Statistician, 39, 33–38.
   Web of Science ®Google Scholar
• Rubin, D. B. (1974), “Estimating the Causal Effect of Treatments in Randomized and Non-Randomized Studies,” Journal of Educational Psychology, 66, 688–701.
   Web of Science ®Google Scholar
• Rubin, D. B. (2004), “Direct and Indirect Causal Effects via Potential Outcomes,” Scandinavian Journal of Statistics, 31, 161–170.
   Web of Science ®Google Scholar
• Sawada, M. (2019), “Non-Compliance in Randomized Control Trials Without Exclusion Restrictions,” Working Paper.
   Google Scholar
• Sharma, A. (2018), “Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables,” arXiv no. 1812.01412.
   Google Scholar
• Simonsen, M., and Skipper, L. (2006), “The Costs of Motherhood: An Analysis Using Matching Estimators,” Journal of Applied Econometrics, 21, 919–934.
   Web of Science ®Google Scholar
• Strittmatter, A. (2019), “Heterogeneous Earnings Effects of the Job Corps by Gender: A Translated Quantile Approach,” Labour Economics, 61, 1–13.
   Web of Science ®Google Scholar
• Tchetgen Tchetgen, E. J., and Shpitser, I. (2012), “Semiparametric Theory for Causal Mediation Analysis: Efficiency Bounds, Multiple Robustness, and Sensitivity Analysis,” The Annals of Statistics, 40, 1816–1845.
   PubMed Web of Science ®Google Scholar
• VanderWeele, T. J. (2008), “Simple Relations Between Principal Stratification and Direct and Indirect Effects,” Statistics & Probability Letters, 78, 2957–2962.
   Web of Science ®Google Scholar
• VanderWeele, T. J. (2009), “Marginal Structural Models for the Estimation of Direct and Indirect Effects,” Epidemiology, 20, 18–26.
   PubMed Web of Science ®Google Scholar
• VanderWeele, T. J. (2012), “Comments: Should Principal Stratification Be Used to Study Mediational Processes?,” Journal of Research on Educational Effectiveness, 5, 245–249.
   PubMed Web of Science ®Google Scholar
• Vansteelandt, S., Bekaert, M., and Lange, T. (2012), “Imputation Strategies for the Estimation of Natural Direct and Indirect Effects,” Epidemiologic Methods, 1, 129–158.
   Google Scholar
• Vinokur, A. D., and Price, R. H. (1999), “Jobs II Preventive Intervention for Unemployed Job Seekers, 1991–1993,” in Inter-University Consortium for Political and Social Research.
   Google Scholar
• Vinokur, A. D., Price, R. H., and Schul, Y. (1995), “Impact of the JOBS Intervention on Unemployed Workers Varying in Risk for Depression,” American Journal of Community Psychology, 23, 39–74. DOI: https://doi.org/10.1007/BF02506922.
   PubMed Web of Science ®Google Scholar
• Wang, X., and Flores-Lagunes, A. (2019), “Conscription and Military Service: Do They Result in Future Violent and Non-Violent Incarcerations and Recidivism?,” Working Paper.
   Google Scholar
• Wüthrich, K. (2020), “A Comparison of Two Quantile Models With Endogeneity,” Journal of Business & Economic Statistics, 38, 443–456.
   Web of Science ®Google Scholar