Causal Inference with Conditional Instruments using Deep Generative Models

TL;DR

This paper introduces a deep generative model-based method to discover conditional instrumental variables and their conditioning sets directly from data, improving causal effect estimation in observational studies with latent confounders.

Contribution

It presents a novel data-driven approach using deep generative models to learn representations of CIVs and their conditioning sets, addressing a key gap in causal inference methods.

Findings

Outperforms existing IV methods on synthetic datasets.

Effective in real-world observational data.

Accurately identifies CIVs and conditioning sets.

Abstract

The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders. A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. However, it is challenging to search for a standard IV from data directly due to the strict conditions. The conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables, allowing a wider choice of possible IVs and enabling broader practical applications of the IV approach. Nevertheless, there is not a data-driven method to discover a CIV and its conditioning set directly from data. To fill this gap, in this paper, we propose to learn the representations of the information of a CIV and its conditioning set from data with latent confounders for average causal effect estimation. By taking advantage of deep generative models, we develop a novel data-driven approach for simultaneously learning the representation of a CIV from measured variables and generating the representation of its conditioning set given measured variables. Extensive experiments on synthetic and real-world datasets show that our method outperforms the existing IV methods.