Causal inferences from many experiments

References

• B.A. Brumback, M.A. Hernan, S.J.P.A. Haneuse, and J.M. Robins, Sensitivity analyses for unmeasured confounding assuming a marginal structural model for repeated measures, Stat. Med. 23 (2004), pp. 749–767. doi: 10.1002/sim.1657
   PubMed Web of Science ®Google Scholar
• W.K.T. Cho, J.J. Sauppe, A.G. Nikolaev, S.H. Jacobson, and E.C. Sewell, An optimization approach for making causal inferences, Statist. Neerland. 67 (2013), pp. 211–226. doi: 10.1111/stan.12004
   Web of Science ®Google Scholar
• K.A. Couch, New evidence on the long-term effects of employment training programs, J. Labor Econ. 10 (1992), pp. 380–388. doi: 10.1086/298292
   Web of Science ®Google Scholar
• R. Dehejia, Practical propensity score matching: A reply to Smith and Todd, J. Economet. 125 (2005), pp. 355–364. doi: 10.1016/j.jeconom.2004.04.012
   Web of Science ®Google Scholar
• R.H. Dehejia and S. Wahba, Causal effects in non-experimental studies: Re-evaluating the evaluation of training programs, J. Amer. Stat. Assoc. 94 (1999), pp. 1053–1062. doi: 10.1080/01621459.1999.10473858
   Web of Science ®Google Scholar
• R.H. Dehejia and S. Wahba, Propensity score matching methods for nonexperimental causal studies, Rev. Econ. Stat. 84 (2002), pp. 151–161. doi: 10.1162/003465302317331982
   Web of Science ®Google Scholar
• A. Diamond and J.S. Sekhon, Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies, Rev. Econ. Stat. 95 (2013), pp. 932–945. doi: 10.1162/REST_a_00318
   Web of Science ®Google Scholar
• R.A. Fisher, Design of Experiments, Hafner, New York, 1935.
   Google Scholar
• B.B. Hansen, Full matching in an observational study of coaching for the SAT, J. Amer. Stat. Assoc. 99 (2004), pp. 609–618. doi: 10.1198/016214504000000647
   Web of Science ®Google Scholar
• J.J. Heckman and V.J. Hotz, Choosing among alternative nonexperimental methods for estimating the impact of social programs: The case of manpower training, J. Amer. Stat. Assoc. 84 (1989), pp. 862–874. doi: 10.1080/01621459.1989.10478848
   Web of Science ®Google Scholar
• J.J. Heckman, H. Ichimura, J. Smith, and P. Todd, Characterizing selection bias using experimental data, Econometrica 66 (1998), pp. 1017–1098. doi: 10.2307/2999630
   Web of Science ®Google Scholar
• P.W. Holland, Statistics and causal inference, J. Amer. Stat. Assoc. 81 (1986), pp. 945–960. doi: 10.1080/01621459.1986.10478354
   Web of Science ®Google Scholar
• D.R. Kinder and T.R. Palfrey, On behalf of an experimental political science, in Experimental Foundations of Political Science, University of Michigan Press, Ann Arbor, 1993, pp. 1–39.
   Google Scholar
• H.W. Kuhn, The Hungarian method for the assignment problem, Naval Res. Logist Q. 2 (1955), pp. 83–97. doi: 10.1002/nav.3800020109
   Google Scholar
• R. LaLonde, Evaluating the econometric evaluations of training programs with experimental data, Amer. Econ. Rev. 76 (1986), pp. 604–20.
   Web of Science ®Google Scholar
• J. Neyman, On the application of probability theory to agricultural experiments. Essay on principles. Section 9 (1923), Stat. Sci. 5 (1923 [1990]), pp. 463–480. reprint. Transl. by Dabrowska and Speed.
   Google Scholar
• J.M. Robins, Association, causation, and marginal structural models, Synthese 121 (1999), pp. 151–179. doi: 10.1023/A:1005285815569
   Web of Science ®Google Scholar
• P.R. Rosenbaum, Optimal matching for observational studies, J. Amer. Stat. Assoc. 84 (1989), pp. 1024–1032. doi: 10.1080/01621459.1989.10478868
   Web of Science ®Google Scholar
• P.R. Rosenbaum, A characterization of optimal designs for observational studies, J. R. Stat. Soc. 53 (1991), pp. 597–610.
   Google Scholar
• P.R. Rosenbaum, Choice as an alternative to control in observational studies (with discussion), Stat. Sci. 14 (1999), pp. 259–304. doi: 10.1214/ss/1009212410
   Web of Science ®Google Scholar
• P.R. Rosenbaum, Observational Studies, 2nd ed., Springer, New York, 2002.
   Google Scholar
• D.B. Rubin, Estimating causal effects of treatments in randomized and nonrandomized studies, J. Educ. Psychol. 66 (1974), pp. 688–701. doi: 10.1037/h0037350
   Web of Science ®Google Scholar
• D.B. Rubin, Bayesian inference for causal effects: The role of randomization, Ann. Stat. 6 (1978), pp. 34–58. doi: 10.1214/aos/1176344064
   Web of Science ®Google Scholar
• C. Shen, X. Li, L. Li, and M.C. Were, Sensitivity analysis for causal inference using inverse probability weighting, Biometr. J. 53 (2011), pp. 822–837. doi: 10.1002/bimj.201100042
   PubMed Web of Science ®Google Scholar
• J.A. Smith and P.E. Todd, Reconciling conflicting evidence on the performance of propensity score matching methods, AEA Papers Proc. 91 (2001), pp. 112–118. doi: 10.1257/aer.91.2.112
   Web of Science ®Google Scholar
• J.A. Smith and P.E. Todd, Does matching overcome LaLonde's critique of nonexperimental estimators? J. Econometr. 125 (2005), pp. 305–353. doi: 10.1016/j.jeconom.2004.04.011
   Web of Science ®Google Scholar
• J. Smith and P. Todd, Rejoinder, J. Econometr. 125 (2005), pp. 365–375. doi: 10.1016/j.jeconom.2004.04.013
   Web of Science ®Google Scholar
• Z. Zhao, Matching estimators and the data from the national supported work demonstration again, IZA Discussion Papers, No. 2375, 2006.
   Google Scholar
• J.R. Zubizarreta, Using mixed integer programming for matching in an observational study of kidney failure after surgery, J. Amer. Stat. Assoc. 107 (2012), pp. 1360–1371. doi: 10.1080/01621459.2012.703874
   Web of Science ®Google Scholar