Abstract
Estimating individual treatment effects (ITE) from observational data is an important topic in many fields. However, this task is challenging because data from observational studies has selection bias: the treatment assigned to an individual related to that individual’s properties. In this paper, we proposed multi-domain adversarial balancing (MDAB), a method incorporates multi-domain adversarial learning with context-aware sample balancing to reduce the selection bias. It simultaneously learns confounder weights and sample weights through an adversarial learning architecture to generate a balanced representation. MDAB is empirically validated in public benchmark datasets, the results demonstrate that MDAB outperforms various state-of-the-art methods in both binary and multiple treatment settings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rubin, D.B.: Causal inference using potential outcomes: design, modeling, decisions. J. Am. Stat. Assoc 100(469), 322–331 (2005)
Austin, P.C.: An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav. Res. 46(3), 399–424 (2011)
Johansson, F., Shalit, U., Sontag, D.: Learning representations for counterfactual inference. In: International Conference on Machine Learning, pp. 3020–3029. PMLR (2016)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Shalit, U., Johansson, F.D., Sontag, D.: Estimating individual treatment effect: generalization bounds and algorithms. In: International Conference on Machine Learning, pp. 3076–3085. PMLR (2017)
Hassanpour, N., Greiner, R.: Counterfactual regression with importance sampling weights. In: IJCAI, pp. 5880–5887 (2019)
Hassanpour, N., Greiner, R.: Learning disentangled representations for counterfactual regression. In: International Conference on Learning Representations (2019)
Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70(1), 41–55 (1983)
Schwab, P., Linhardt, L., Karlen, W.: Perfect match: a simple method for learning representations for counterfactual inference with neural networks (2018). arXiv preprint arXiv:1810.00656
Hill, J.L.: Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20(1), 217–240 (2011)
Dorie, V.: Non-parametrics for causal inference (2016)
LaLonde, R.J.: Evaluating the econometric evaluations of training programs with experimental data. Am. Econ. Rev. 76, 604–620 (1986)
Crump, R.K., Hotz, V.J., Imbens, G.W., Mitnik, O.A.: Nonparametric tests for treatment effect heterogeneity. Rev. Econ. Stat. 90(3), 389–405 (2008)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Wager, S., Athey, S.: Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc 113(523), 1228–1242 (2018)
Yoon, J., Jordon, J., Van Der Schaar, M.: Ganite: estimation of individualized treatment effects using generative adversarial nets. In: International Conference on Learning Representations (2018)
Schwab, P., Linhardt, L., Bauer, S., Buhmann, J.M., Karlen, W.: Learning counterfactual representations for estimating individual dose-response curves. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5612–5619 (2020)
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(2), 281–305 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, P., Li, Z., Ogino, M. (2021). Multi-Domain Adversarial Balancing for the Estimation of Individual Treatment Effect. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-92307-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92306-8
Online ISBN: 978-3-030-92307-5
eBook Packages: Computer ScienceComputer Science (R0)