Treatment Effect Estimation with Disentangled Latent Factors

Authors

  • Weijia Zhang University of South Australia
  • Lin Liu University of South Australia
  • Jiuyong Li University of South Australia

Keywords:

Causal Learning, Causality

Abstract

Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i.e., variables that affect both the treatment and the outcome. Unfortunately, this assumption is frequently violated in real-world applications, since some variables only affect the treatment but not the outcome, and vice versa. Moreover, in many cases only the proxy variables of the underlying confounding factors can be observed. In this work, we first show the importance of differentiating confounding factors from instrumental and risk factors for both average and conditional average treatment effect estimation, and then we propose a variational inference approach to simultaneously infer latent factors from the observed variables, disentangle the factors into three disjoint sets corresponding to the instrumental, confounding, and risk factors, and use the disentangled factors for treatment effect estimation. Experimental results demonstrate the effectiveness of the proposed method on a wide range of synthetic, benchmark, and real-world datasets.

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Published

2021-05-18

How to Cite

Zhang, W., Liu, L., & Li, J. (2021). Treatment Effect Estimation with Disentangled Latent Factors. Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), 10923-10930. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17304

Issue

Section

AAAI Technical Track on Machine Learning V