A Double Machine Learning Approach to Combining Experimental and Observational Data

TL;DR

This paper introduces a double machine learning framework that combines experimental and observational data, enabling assumption testing and consistent treatment effect estimation even under violations, with demonstrated practical benefits.

Contribution

It presents a novel approach that allows for assumption testing and robust treatment effect estimation by integrating experimental and observational data using double machine learning.

Findings

Framework enables falsification tests for validity assumptions

Consistent treatment effect estimators under assumption violations

Outperforms existing data fusion methods in comparative analyses

Abstract

Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational studies, allowing practitioners to test for assumption violations and estimate treatment effects consistently. Our framework proposes a falsification test for external validity and ignorability under milder assumptions. We provide consistent treatment effect estimators even when one of the assumptions is violated. However, our no-free-lunch theorem highlights the necessity of accurately identifying the violated assumption for consistent treatment effect estimation. Through comparative analyses, we show our framework's superiority over existing data fusion methods. The practical utility of our approach is further exemplified by three real-world case studies, underscoring its potential for widespread application in empirical research.