Conditional Instrumental Variable Regression with Representation Learning for Causal Inference

Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le

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Keywords: Causal Inference, Variational AutoEncoder, Instrumental Variable

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TL;DR: We propose a novel confounding balance representation learning method for non-linear CIV regression in average causal effect estimation from observational data with latent confounders.

Abstract: This paper studies the challenging problem of estimating causal effects from observational data, in the presence of unobserved confounders. The two-stage least square (TSLS) method and its variants with a standard instrumental variable (IV) are commonly used to eliminate confounding bias, including the bias caused by unobserved confounders, but they rely on the linearity assumption. Besides, the strict condition of unconfounded instruments posed on a standard IV is too strong to be practical. To address these challenging and practical problems of the standard IV method (linearity assumption and the strict condition), in this paper, we use a conditional IV (CIV) to relax the unconfounded instrument condition of standard IV and propose a non-linear \underline{CIV} regression with \underline{C}onfounding \underline{B}alancing \underline{R}epresentation \underline{L}earning, CBRL.CIV, for jointly eliminating the confounding bias from unobserved confounders and balancing the observed confounders, without the linearity assumption. We theoretically demonstrate the soundness of CBRL.CIV. Extensive experiments on synthetic and two real-world datasets show the competitive performance of CBRL.CIV against state-of-the-art IV-based estimators and superiority in dealing with the non-linear situation.

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Primary Area: causal reasoning

Submission Number: 3957