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Comment: Will competition-winning methods for causal inference also succeed in practice? (English) Zbl 1420.62352
Summary: First, we would like to congratulate the authors for successfully hosting the causal inference data competition (referred to as Competition henceforth) and contributing a unique and thought-provoking article to the literature. The authors have provided a comprehensive and timely platform to evaluate the ever-growing number of methods used for covariate adjustment in observational studies. In our comment, we don’t generally question the results of the competition, but we do wish to emphasize several other key elements about the role statistics plays in causal inference and observational studies.
Comment on [V. Dorie et al., “Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition”, ibid. 34, No. 1, 43–68 (2019; Zbl 1420.62345)].

62K20 Response surface designs
68T05 Learning and adaptive systems in artificial intelligence
62B15 Theory of statistical experiments
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[1] Angrist, J. D. and Krueger, A. B. (1999). Empirical strategies in labor economics. In Handbook of Labor Economics (O. Ashenfelter and D. Card, eds.) 3A 1277-1366. Elsevier, Amsterdam.
[2] Box, G. E. (1979). Some problems of statistics and everyday life. J. Amer. Statist. Assoc.74 1-4.
[3] Breiman, L. (2001). Statistical modeling: The two cultures. Statist. Sci.16 199-231. · Zbl 1059.62505
[4] Cook, T. D., Campbell, D. T. and Shadish, W. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin, Boston, MA.
[5] Cook, T. D., Shadish, W. R. and Wong, V. C. (2008). Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons. J. Policy Anal. Manage.27 724-750.
[6] Hill, A. B. (1965). The environment and disease: Association or causation? J. R. Soc. Med.58 295-300.
[7] Imbens, G. W. (2003). Sensitivity to exogeneity assumptions in program evaluation. Am. Econ. Rev. Pap. Proc.93 126-132.
[8] Keele, L. and Small, D. (2018). Comparing covariate prioritization via matching to machine learning methods for causal inference using five empirical applications. Preprint. Available at arXiv:1805.03743.
[9] Lipsitch, M., Tchetgen Tchetgen, E. and Cohen, T. (2010). Negative controls: A tool for detecting confounding and bias in observational studies. Epidemiology21 383-388.
[10] Pimentel, S. D., Kelz, R. R., Silber, J. H. and Rosenbaum, P. R. (2015). Large, sparse optimal matching with refined covariate balance in an observational study of the health outcomes produced by new surgeons. J. Amer. Statist. Assoc.110 515-527.
[11] Rosenbaum, P. R. (1987). Sensitivity analysis for certain permutation inferences in matched observational studies. Biometrika74 13-26. · Zbl 0605.62130
[12] Rosenbaum, P. R. (2001). Replicating effects and biases. Amer. Statist.55 223-227.
[13] Rosenbaum, P. R. (2002). Observational Studies, 2nd ed. Springer Series in Statistics. Springer, New York. · Zbl 0985.62091
[14] Rosenbaum, P. R. (2005). Heterogeneity and causality: Unit heterogeneity and design sensitivity in observational studies. Amer. Statist.59 147-152.
[15] Rosenbaum, P. R. (2006). Differential effects and generic biases in observational studies. Biometrika93 573-586. · Zbl 1108.62135
[16] Rubin, D. B. (2008). For objective causal inference, design trumps analysis. Ann. Appl. Stat.2 808-804. · Zbl 1149.62089
[17] Shadish, W. R., Clark, M. H. and Steiner, P. M. (2008). Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments. J. Amer. Statist. Assoc.103 1334-1343. · Zbl 1286.62013
[18] Zhao, Q. (2019). Covariate balancing propensity score by tailored loss functions. Ann. Statist.47 965-993. · Zbl 1420.62464
[19] Zubizarreta, J. R. (2012). Using mixed integer programming for matching in an observational study of kidney failure after surgery. J. Amer. Statist. Assoc.107 1360-1371. · Zbl 1258.62119
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