Estimating Individualized Causal Effect with Confounded Instruments
Haotian Wang, Wenjing Yang, Longqi Yang, Anpeng Wu, Liyang Xu, Jing Ren, Fei Wu, Kun Kuang
Abstract
Learning individualized causal effect (ICE) plays a vital role in various fields of big data analysis, ranging from fine-grained policy evaluation to personalized treatment development. However, the presence of unmeasured confounders increases the difficulty of estimating ICE in real-world scenarios. A wide range of methods have been proposed to address the unmeasured confounders with the aid of instrument variable (IV), which sources from the treatment randomization. The performance of these methods relies on the well-predefined IVs that satisfy the unconfounded instruments assumption (i.e., the IVs are independent with the unmeasured confounders given observed covariates), which is untestable and leads to finding a valid IV becomes an art rather than science. In this paper, we focus on estimating the ICE with confounded instruments that violate the unconfounded instruments assumption. By considering the conditional independence between the set of confounded instruments and the outcome variable, we propose a novel method, named CVAE-IV, to generate a substitute of the unmeasured confounder with a conditional variational autoencoder. Our theoretical analysis guarantees that the generated confounder substitute will identify unbiased ICE. Extensive experiments on bias demand prediction and Mendelian randomization analysis verify the effectiveness of our method.