A Double Machine Learning Approach to Combining Experimental and Observational Data

• URL: http://arxiv.org/abs/2307.01449v2
• Date: Wed, 3 Apr 2024 02:26:24 GMT
• Title: A Double Machine Learning Approach to Combining Experimental and Observational Data
• Authors: Harsh Parikh, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky,
• Abstract summary: We propose a double machine learning approach to combine experimental and observational studies. Our framework tests for violations of external validity and ignorability under milder assumptions.
• Score: 59.29868677652324
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 tests for violations of external validity and ignorability under milder assumptions. When only one of these assumptions is violated, we provide semiparametrically efficient treatment effect estimators. 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.

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