A Double Machine Learning Approach to Combining Experimental and Observational Data

arXiv:2307.01449v2 Announce Type: replace-cross
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 …

abstract arxiv assumptions cs.ai cs.lg data econ.em effects experimental framework machine machine learning stat.me studies test tests treatment type