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Robust causal inference via subclassification and covariate balancing methods (本文)

中村, 知繁 慶應義塾大学

2021.02.01

概要

Subclassification and covariate balancing methods are two representative approaches of robust estimation for causal effects in observational studies. To estimate causal effects in observational studies, propensity score weighting estimators are often used under the strongly ignorable treatment assignment assumption. However, in many cases, because propensity scores must be estimated from data, a model misspecification of propensity score may occur in practice. As a consequence a causal estimator may suffer from severe bias due to the misspecification. Subclassification and covariate balancing methods are known as robust estimation approaches for the propensity score model misspecification problems.

This thesis is organized as follows. Chapter 1 is a brief introduction to causal inference including causal estimand such as average treatment effects and the related estimation methods.

In Chapter 2, robust causal inference by a covariate balancing method is introduced. Firstly, we introduce a covariate balancing method and its difference from the standard approach that models treatment assignment by parametric models such as a logistic regres- sion model. Then we show the reason why a covariate balancing method has the robustness compared to the standard parametric approach. We apply a covariate balancing method to estimate the effect of a squeeze play in major league baseball and compare the result with those by parametric propensity score models.

In Chapter 3, robust causal inference with subclassification method is introduced and a causal subclassification tree is proposed. The bias of subclassification estimators caused by the fixed number of subclasses is pointed out in many literatures. We show the subclas-
sification estimator constructed by our method has a √N - consistency that the standard subclassification estimator does not have. Then we perform simulation experiments and show that the causal subclassification tree algorithm improves the performance of weight- ing estimators for causal effects, especially when the propensity model and/or outcome model is misspecified.

Chapter 4 is a conclusion of this thesis. We summarize the discussion of robust causal inference with subclassification and covariate balancing methods and then the remaining issues of subclassification methods such as within-subclass bias problems are discussed.

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参考文献

Abadie, A., & Imbens, G.W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. https://doi.org/10.1111/j.1468-0262.2006.00655.x

Albert, J., & Bennet, J. (2001). Curve ball: Baseball, Statistics, and the Role of Chance in the Game, Springer, New York.

Athey, S., & Imbens, G.W. (2016). Recursive partitioning for heterogeneous causal effects.

Proceedings of the National Academy of Sciences, 113(27), 7353-7360. https://doi.org/10.1073/pnas.1510489113

Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. Annals of Statistics, 47(2), 1148-1178. https://doi.org/10.1214/18-AOS1709

Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324

Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. (1984). Classification and Regression Trees. Chapman and Hall/CRC, first edition.

Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Sturmer, T. (2006). Variable Selection for Propensity Score Models. American Journal of Epidemiology, 163(12), 1149-1156. https://doi.org/10.1093/aje/kwj149

Cannas, M., & Arpino, B. (2019). A comparison of machine learning algorithms and covariate balance measures for propensity score matching and weighting. Biometrical Journal, 61(4), 1049-1072. https://doi.org/10.1002/bimj.201800132

Chan, K.C.G., Yam, S.C.P. & Zhang, Z. (2016). Globally efficient non - parametric 54 inference of average treatment effects by empirical balancing calibration weighting. J. R. Stat. Soc. B, 78, 673-700. https://doi.org/10.1111/rssb.12129

Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, 785-794. https://doi.org/10.1145/2939672.2939785

Drake, C. (1993). Effects of Misspecification of the Propensity Score on Estimators of Treatment Effect. Biometrics, 49(4), 1231-1236. https://doi.org/10.2307/2532266

Feng, P., Zhou, X. H., Zou, Q. M., Fan, M. Y., & Li ,X. S. (2012). Generalized propensity score for estimating the average treatment effect of multiple treatments. Statistics in Medicine, 31(7), 681-697. https://doi.org/10.1002/sim.4168

Greenland, S., Pearl, J., & Robins, J. (1999). Confounding and Collapsibility in Causal Inference, Statistical Science, 16, 1, 29–46.

Hansen, B. B. (2004). Full Matching in an Observational Study of Coaching for the SAT. Journal of the American Statistical Association, 99, 609-618. https://doi.org/10.1198/016214504000000647

Hansen, P. (1982). Large sample properties of generalized method of moments estimators, Econometrica, 50, 1029–1054.

Hansen, P., Heaton, J. & Yaron, A. (1996). Finite-sample properties of some alternative GMM estimators, Journal of Business & Economic Statistics, 14, 262-280.

Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching As An Econometric Evalu- ation Estimator: Evidence from Evaluating a Job Training Programme. The Review of Economic Studies, 64(4), 605-654. https://doi.org/10.2307/2971733

Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217-240. https://doi.org/10.1198/jcgs.2010.08162

Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189. https://doi.org/10.1111/1468-0262.00442

Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without re- placement from a finite universe. Journal of the American Statistical Association, 47, 663-685. https://doi.org/10.1080/01621459.1952.10483446

Imai, K., & Ratkovic, M. (2014). Covariate balancing propensity score. Journal of the Royal Statistical Society: Series B, 76, 243-263. https://doi.org/10.1111/rssb.12027

Jianqing, F., Imai, K., Liu, H., Ning, Y., & Yang, X. (2016). Improving Covariate Balanc- ing Propensity Score: A Doubly Robust and Efficient Approach. https://imai.fas.harvard.edu/research/files/CBPStheory.pdf

Kang, J. D. Y., & Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statis- tical Science, 22, 523-539. https://doi.org/10.1214/07-STS227

Lee, H. (2011), Is the small-ball strategy effective in winning games? A stochastic frontier production approach, Journal of Productivity Analysis, 35, 51-59

Li, F., Morgan, K. L., & Zaslavsky, A. M. (2018). Balancing covariates via propensity score weighting. Journal of the American Statistical Association, 113, 390-400. https://doi.org/10.1080/01621459.2016.1260466

Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23, 2937-2960. https://doi.org/10.1002/sim.1903

Nakamura, T., & Minami, M. (2021) Causal subclassification tree algorithm and robust causal effect estimation via subclassification. International Journal of Statistics and Probability, 10, 40-57, https://doi.org/10.5539/ijsp.v10n1p40

Newey, W., & McFadden, D. (1994). Large sample estimation and hypothesis testing, Handbook of Econometrics, 4, 2111-2245

Nie, X., & Wager, S. (2020). Quasi-Oracle Estimation of Heterogeneous Treatment Effects. Biometrika, forthcoming, https://doi.org/10.1093/biomet/asaa076

Qin, J., & Zhang, B. (2007). Empirical - likelihood - based inference in missing response problems and its application in observational studies. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69, 101-122. https://doi.org/10.1111/j.1467-9868.2007.00579.x

Robins, J. M., Hern´an, M. A., & B. Brumback (2000): Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550-560. https://doi.org/10.1097/00001648-200009000-00011

Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89, 846-866. https://doi.org/10.1080/01621459.1994.10476818

Rosenbaum, P. R. (1987). Model-based direct adjustment. Journal of the American Sta- tistical Association, 82, 387-394. https://doi.org/10.1080/01621459.1987.10478441

Rosenbaum, P. R. (1989). Optimal matching for observational studies. Journal of the American Statistical Association, 84, 1024-1032. https://doi.org/10.1080/01621459.1989.10478868

Rosenbaum, P. R. (1991). A characterization of optimal designs for observational studies. Journal of the Royal Statistical Society: Series B, 53, 597-610. https://doi.org/10.1111/j.2517-6161.1991.tb01848.x

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55. https://doi.org/10.1093/biomet/70.1.41

Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Associ- ation, 79, 516-524. https://doi.org/10.1080/01621459.1984.10478078

Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33-38. https://doi.org/10.1080/00031305.1985.10479383

Tan, Z. (2010). Bounded, efficient and doubly robust estimation with inverse weighting, Biometrika, 97, 661-682. https://doi.org/10.1093/biomet/asq035

Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113, 1228-1242. https://doi.org/10.1080/01621459.2017.1319839

Wager, S., & Walther, G. (2016). Adaptive concentration of regression trees, with appli- cation to random forests. https://arxiv.org/abs/1503.06388v3

Wang, L., Zhang, Y., Richardson, T. S., & Zhou, X. (2020). Robust estimation of propen- sity score weights via subclassification. https://arxiv.org/abs/1602.06366v2

Wang, Y., & Zubizarreta, J. R. (2020). Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations. Biometrika, 107, 93- 105. https://doi.org/10.1093/biomet/asz050

Ning, Y., Sida, P., & Imai, K. (2020) Robust estimation of causal effects via a high- dimensional covariate balancing propensity score. Biometrika, 107, 533-554. https://doi.org/10.1093/biomet/asaa020

中村 友繫, 南 美穂子 (2017). Covariate Balancing Propensity Score を用いた、スクイズ作戦の有効性の解析. 統計数理, 65, 217-234.

中村 友繫, 南 美穂子 (2016). 傾向スコアを用いたバント効果の推定ーノーアウト1塁のバントは, 得点確率を有意に高めるか⁉, 岩波データサイエンス, 岩波書店.

仁科 敏久, 鳥越 規央 (2012). プロ野球のセオリー：「データ」は「経験」を超えるのか, ベストセラーズ.

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