A Double Machine Learning Approach to Combining Experimental and Observational Data
This addresses the issue of untestable assumptions in empirical research for practitioners, though it is incremental as it builds on existing data fusion methods.
The paper tackles the problem of combining experimental and observational data to estimate treatment effects by proposing a double machine learning approach that tests for assumption violations and provides consistent estimators even when assumptions are violated, with comparative analyses showing superiority over existing methods.
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.