Variable Importance Matching for Causal Inference

TL;DR

This paper introduces Model-to-Match, a flexible, scalable framework for causal inference that uses variable importance to create matching methods, with theoretical guarantees and empirical validation.

Contribution

It proposes a novel framework for causal inference that leverages variable importance for matching, scalable to high-dimensional data, with theoretical guarantees and practical extensions.

Findings

Method achieves auditability and accuracy in treatment effect estimation.

LASSO-based implementation scales well with many confounders.

Experimental results confirm effectiveness and extendability to nonparametric models.

Abstract

Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimensional data. We describe a general framework called Model-to-Match that achieves these goals by (i) learning a distance metric via outcome modeling, (ii) creating matched groups using the distance metric, and (iii) using the matched groups to estimate treatment effects. Model-to-Match uses variable importance measurements to construct a distance metric, making it a flexible framework that can be adapted to various applications. Concentrating on the scalability of the problem in the number of potential confounders, we operationalize the Model-to-Match framework with LASSO. We derive performance guarantees for settings where LASSO outcome modeling consistently identifies all confounders (importantly without requiring the linear model to be correctly specified). We also provide experimental results demonstrating the method's auditability, accuracy, and scalability as well as extensions to more general nonparametric outcome modeling.