Stable estimation of heterogeneous treatment effects
AUTHORs: 
Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei WuAuthors Info & Claims
Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei WuAuthors Info & ClaimsArticle No.: 1560, Pages 37496 - 37510
Abstract
Estimating heterogeneous treatment effects (HTE) is crucial for identifying the variation of treatment effects across individuals or subgroups. Most existing methods estimate HTE by removing the confounding bias from imbalanced treatment assignments. However, these methods may produce unreliable estimates of treatment effects and potentially allocate suboptimal treatment arms for underrepresented populations. To improve the estimation accuracy of HTE for underrepresented populations, we propose a novel Stable CounterFactual Regression (StableCFR) to smooth the population distribution and upsample the underrepresented subpopulations, while balancing confounders between treatment and control groups. Specifically, StableCFR upsamples the underrepresented data using uniform sampling, where each disjoint subpopulation is weighted proportional to the Lebesgue measure of its support. Moreover, StableCFR balances covariates by using an epsilon-greedy matching approach. Empirical results on both synthetic and real-world datasets demonstrate the superior performance of our StableCFR on estimating HTE for underrepresented populations.
References
[1]
Abadie, A. and Imbens, G. W. Large sample properties of matching estimators for average treatment effects. econometrica, 74(1):235-267, 2006.
[2]
Abadie, A. and Imbens, G. W. Bias-corrected matching estimators for average treatment effects. Journal of Business & Economic Statistics, 29(1):1-11, 2011.
[3]
Angrist, J. D. and Pischke, J.-S. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2009.
[4]
Athey, S., Imbens, G. W., and Wager, S. Approximate residual balancing: debiased inference of average treatment effects in high dimensions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(4): 597-623, 2018.
[5]
Branco, P., Torgo, L., and Ribeiro, R. P. Smogn: a pre-processing approach for imbalanced regression. In First international workshop on learning with imbalanced domains: Theory and applications, pp. 36-50. PMLR, 2017.
[6]
Branco, P., Torgo, L., and Ribeiro, R. P. Rebagg: Resampled bagging for imbalanced regression. In Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, pp. 67-81. PMLR, 2018.
[7]
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321-357, 2002.
[8]
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S. Class-balanced loss based on effective number of samples. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9268-9277, 2019.
[9]
Erba, V., Gherardi, M., and Rotondo, P. Intrinsic dimension estimation for locally undersampled data. Scientific reports, 9(1):1-9, 2019.
[10]
Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., and Davidian, M. Doubly robust estimation of causal effects. American journal of epidemiology, 173(7):761-767, 2011.
[11]
Hassanpour, N. and Greiner, R. Counterfactual regression with importance sampling weights. In IJCAI, pp. 5880- 5887, 2019a.
[12]
Hassanpour, N. and Greiner, R. Learning disentangled representations for counterfactual regression. In International Conference on Learning Representations, 2019b.
[13]
He, H. and Garcia, E. A. Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21(9):1263-1284, 2009.
[14]
He, Y.-Y., Wu, J., and Wei, X.-S. Distilling virtual examples for long-tailed recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 235-244, 2021.
[15]
Hong, Y., Han, S., Choi, K., Seo, S., Kim, B., and Chang, B. Disentangling label distribution for long-tailed visual recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6626- 6636, 2021.
[16]
Imai, K., Keele, L., and Yamamoto, T. Identification, inference and sensitivity analysis for causal mediation effects. 2010.
[17]
Imbens, G. W., Rubin, D. B., et al. Causal inference for statistics, social, and biomedical sciences. Cambridge Books, 2015.
[18]
Johansson, F., Shalit, U., and Sontag, D. Learning representations for counterfactual inference. In International conference on machine learning, pp. 3020-3029. PMLR, 2016.
[19]
LaLonde, R. J. Evaluating the econometric evaluations of training programs with experimental data. The American economic review, pp. 604-620, 1986.
[20]
Li, S., Vlassis, N., Kawale, J., and Fu, Y. Matching via dimensionality reduction for estimation of treatment effects. In IJCAI, pp. 3768-3774, 2016.
[21]
Liu, J., Sun, Y., Han, C., Dou, Z., and Li, W. Deep representation learning on long-tailed data: A learnable embedding augmentation perspective. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2970-2979, 2020.
[22]
Mooij, J. M., Peters, J., Janzing, D., Zscheischler, J., and Schölkopf, B. Distinguishing cause from effect using observational data: methods and benchmarks. The Journal of Machine Learning Research, 17(1):1103-1204, 2016.
[23]
Pearl, J. Causal inference in statistics: An overview. Statistics surveys, 3:96-146, 2009a.
[24]
Pearl, J. Causality. Cambridge university press, 2009b.
[25]
Ren, J., Zhang, M., Yu, C., and Liu, Z. Balanced mse for imbalanced visual regression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7926-7935, 2022.
[26]
Rosenbaum, P. R. Model-based direct adjustment. Journal of the American statistical Association, 82(398):387-394, 1987.
[27]
Rosenbaum, P. R. and Rubin, D. B. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1):41-55, 1983.
[28]
Saltelli, A. Sensitivity analysis for importance assessment. Risk analysis, 22(3):579-590, 2002.
[29]
Shalit, U., Johansson, F. D., and Sontag, D. Estimating individual treatment effect: generalization bounds and algorithms. In International Conference on Machine Learning, pp. 3076-3085. PMLR, 2017.
[30]
Shen, L., Lin, Z., and Huang, Q. Relay backpropagation for effective learning of deep convolutional neural networks. In European conference on computer vision, pp. 467-482. Springer, 2016.
[31]
Steininger, M., Kobs, K., Davidson, P., Krause, A., and Hotho, A. Density-based weighting for imbalanced regression. Machine Learning, 110(8):2187-2211, 2021.
[32]
Tang, K., Tao, M., Qi, J., Liu, Z., and Zhang, H. Invariant feature learning for generalized long-tailed classification. In European Conference on Computer Vision, pp. 709- 726. Springer, 2022.
[33]
Torgo, L., Ribeiro, R. P., Pfahringer, B., and Branco, P. Smote for regression. In Portuguese conference on artificial intelligence, pp. 378-389. Springer, 2013.
[34]
VanderWeele, T. J. and Ding, P. Sensitivity analysis in observational research: introducing the e-value. Annals of internal medicine, 167(4):268-274, 2017.
[35]
Wu, A., Yuan, J., Kuang, K., Li, B., Wu, R., Zhu, Q., Zhuang, Y. T., and Wu, F. Learning decomposed representations for treatment effect estimation. IEEE Transactions on Knowledge and Data Engineering, 2022.
[36]
Wyatt, L. H., Peterson, G. C. L., Wade, T. J., Neas, L. M., and Rappold, A. G. Annual pm2. 5 and cardiovascular mortality rate data: Trends modified by county socioeconomic status in 2,132 us counties. Data in brief, 30: 105-318, 2020.
[37]
Xiang, L., Ding, G., and Han, J. Learning from multiple experts: Self-paced knowledge distillation for longtailed classification. In European Conference on Computer Vision, pp. 247-263. Springer, 2020.
[38]
Yang, Y., Zha, K., Chen, Y., Wang, H., and Katabi, D. Delving into deep imbalanced regression. In International Conference on Machine Learning, pp. 11842-11851. PMLR, 2021.
[39]
Yao, L., Li, S., Li, Y., Huai, M., Gao, J., and Zhang, A. Representation learning for treatment effect estimation from observational data. Advances in Neural Information Processing Systems, 31, 2018.
[40]
Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., and Zhang, A. A survey on causal inference. arXiv preprint arXiv:2002.02770, 2020.
[41]
Yi, X., Tang, K., Hua, X.-S., Lim, J.-H., and Zhang, H. Identifying hard noise in long-tailed sample distribution. In European Conference on Computer Vision, pp. 739- 756. Springer, 2022.
[42]
Zubizarreta, J. R. Stable weights that balance covariates for estimation with incomplete outcome data. Journal of the American Statistical Association, 110(511):910-922, 2015.
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Published: 23 July 2023
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