Treatment effect estimation with disentangled latent factors
This addresses a key limitation in causal inference for real-world applications where observed variables include non-confounding factors, offering a method to improve treatment effect estimation accuracy.
The paper tackled the problem of estimating treatment effects from observational data by differentiating confounding factors from instrumental and risk factors, and proposed a variational inference approach to disentangle latent factors for improved estimation, demonstrating effectiveness on synthetic, benchmark, and real-world datasets.
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.