Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data

TL;DR

This paper introduces a data-driven method to discover valid instrumental variables for causal inference from observational data, leveraging partial ancestral graphs to improve the accuracy of causal effect estimation.

Contribution

It presents a novel algorithm that identifies ancestral instrumental variables and their conditioning sets from data, reducing reliance on domain knowledge and invalid IVs.

Findings

Accurately estimates causal effects on synthetic datasets.

Outperforms state-of-the-art IV estimators on real-world data.

Supports causal inference with fewer assumptions.

Abstract

Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confounders between the treatment and the outcome. However, existing IV methods require that an IV is selected and justified with domain knowledge. An invalid IV may lead to biased estimates. Hence, discovering a valid IV is critical to the applications of IV methods. In this paper, we study and design a data-driven algorithm to discover valid IVs from data under mild assumptions. We develop the theory based on partial ancestral graphs (PAGs) to support the search for a set of candidate Ancestral IVs (AIVs), and for each possible AIV, the identification of its conditioning set. Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data. The experiments on synthetic and real-world datasets show that the developed IV discovery algorithm estimates accurate estimates of causal effects in comparison with the state-of-the-art IV based causal effect estimators.