Learning Conditional Instrumental Variable Representation for Causal Effect Estimation

TL;DR

This paper introduces DVAE.CIV, a novel disentangled representation learning method that estimates causal effects from observational data with unmeasured confounders, overcoming limitations of traditional IV approaches.

Contribution

The paper proposes a new method, DVAE.CIV, that learns and disentangles representations of conditional instrumental variables and their conditioning sets for improved causal effect estimation.

Findings

DVAE.CIV outperforms existing estimators on synthetic datasets.

The method demonstrates superior accuracy on real-world data.

Disentangled representations enhance causal inference robustness.

Abstract

One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data. However, causal effect estimation often suffers from the impacts of confounding bias caused by unmeasured confounders that affect both the treatment and the outcome. The instrumental variable (IV) approach is a powerful way to eliminate the confounding bias from latent confounders. However, the existing IV-based estimators require a nominated IV, and for a conditional IV (CIV) the corresponding conditioning set too, for causal effect estimation. This limits the application of IV-based estimators. In this paper, by leveraging the advantage of disentangled representation learning, we propose a novel method, named DVAE.CIV, for learning and disentangling the representations of CIV and the representations of its conditioning set for causal effect estimations from data with latent confounders. Extensive experimental results on both synthetic and real-world datasets demonstrate the superiority of the proposed DVAE.CIV method against the existing causal effect estimators.