Learning Conditional Instrumental Variable Representation for Causal Effect Estimation
This addresses the challenge of confounding bias in causal inference for researchers and practitioners, offering a novel approach that eliminates the need for pre-nominated instrumental variables, though it is incremental in advancing representation learning techniques.
The paper tackles the problem of causal effect estimation from observational data with latent confounders by proposing DVAE.CIV, a method that learns and disentangles representations of conditional instrumental variables and their conditioning sets, demonstrating superiority over existing estimators in experiments on synthetic and real-world datasets.
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.