MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models

TL;DR

This paper introduces a Bayesian method for causal inference using many-dimensional instrumental variables, effectively handling model uncertainty and weak assumptions to improve causal effect estimation in large datasets.

Contribution

It presents a novel Bayesian model averaging approach for instrumental variable selection that is computationally efficient and robust to model uncertainty in high-dimensional settings.

Findings

The method outperforms existing approaches in simulated data.

It demonstrates robustness and accuracy on real-world GWAS data.

The approach effectively accounts for model uncertainty in causal inference.

Abstract

The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal inference. One popular approach is to utilize these many-dimensional measurements as instrumental variables (instruments) for improving the causal effect estimate between other pairs of variables. Unfortunately, searching for proper instruments in a many-dimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid, so most existing search methods either rely on overly stringent modeling assumptions or fail to capture the inherent model uncertainty in the selection process. We show that, as long as at least some of the candidates are (close to) valid, without knowing a priori which ones, they collectively still pose enough restrictions on the target interaction to obtain a reliable causal effect estimate. We propose a general and efficient causal inference algorithm that accounts for model uncertainty by performing Bayesian model averaging over the most promising many-dimensional instrumental variable models, while at the same time employing weaker assumptions regarding the data generating process. We showcase the efficiency, robustness and predictive performance of our algorithm through experimental results on both simulated and real-world data.