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Research Articles

Joint Deep Learning for Covariate Decomposition and Treatment Effect Estimation

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Pages 547-559 | Received 21 Jul 2022, Accepted 01 Sep 2022, Published online: 13 Dec 2022

References

  • Baringhaus, L., & Franz, C. (2004). On a new multivariate two-sample test. Journal of Multivariate Analysis, 88, 190–206. https://doi.org/10.1016/S0047-259X(03)00079-4
  • Bloniarz, A., Liu, H., Zhang, C. H., Sekhon, J. S., & Yu, B. (2016). Lasso adjustments of treatment effect estimates in randomized experiments. Proceedings of the National Academy of Sciences of the United States of America, 113, 7383–7390. https://doi.org/10.1073/pnas.1510506113
  • Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Stürmer, T. (2006). Variable selection for propensity score models. American Journal of Epidemiology, 163, 1149–1156. https://doi.org/10.1093/aje/kwj149
  • Ding, P., Vanderweele, T., & Robins, J. M. (2017). Instrumental variables as bias amplifiers with general outcome and confounding. Biometrika, 104, 291–302. https://doi.org/10.1093/biomet/asx009
  • Gretton, A., Borgwardt, K. M., Rasch, M. J., Schoelkopf, B., & Smola, A. (2012). A kernel two-sample test. The Journal of Machine Learning Research, 13, 723–773.
  • Hassanpour, N., Greiner, R. (2019). Counterfactual regression with importance sampling weights. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization.
  • Hassanpour, N., & Greiner, R. (2020). Learning disentangled representations for counterfactual regression. In International Conference on Learning Representations.
  • Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47, 5–86. https://doi.org/10.1257/jel.47.1.5
  • Johansson, F. D., Shalit, U., & Sontag, D. (2016). Learning representations for counterfactual inference. In 33rd International Conference on Machine Learning, ICML 2016. International Machine Learning Society (IMLS).
  • Koch, B., Vock, D. M., & Wolfson, J. (2018). Covariate selection with group lasso and doubly robust estimation of causal effects. Biometrics, 74, 8–17. https://doi.org/10.1111/biom.12736
  • Kuang, K., Cui, P., Zou, H., Li, B., Tao, J., Wu, F., & Yang, S. (2020). Data-driven variable decomposition for treatment effect estimation. IEEE Transactions on Knowledge and Data Engineering, 34, 1–7.
  • Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., Welling, M. (2017). Causal effect inference with deep latent-variable models. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Curran Associates Inc.
  • Paulson, A. S., Holcomb, E. W., & Leitch, R. A. (1975). The estimation of the parameters of the stable laws. Biometrika, 62, 163–170. https://doi.org/10.1093/biomet/62.1.163
  • Pearl, J. (2010). On a class of bias-amplifying variables that endanger effect estimates. In Proceedings of Uncertainty and Artificial Intelligence.
  • 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
  • Rubin, D. B. (1974). Estimating causal effects if treatment in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701. https://doi.org/10.1037/h0037350
  • Ruschendorf, L. (1985). The Wasserstein distance and approximation theorems. Probability Theory and Related Fields, 70, 117–129.
  • Wu, A., Kuang, K., Yuan, J., Li, B., Zhou, P., Tao, J., Zhu, Q., Zhuang, Y., & Wu, F. (2020). Learning decomposed representation for counterfactual inference. arXiv:2006.07040v2.
  • Yao, L., Li, S., Li, Y., Huai, M., Gao, J., & Zhang, A. (2018). Representation learning for treatment effect estimation from observational data. In Advances in Neural Information Processing Systems. Curran Associates, Inc.

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