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A Double Machine Learning Approach to Combining Experimental and Observational Data

Author

Listed:
  • Harsh Parikh
  • Marco Morucci
  • Vittorio Orlandi
  • Sudeepa Roy
  • Cynthia Rudin
  • Alexander Volfovsky

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.

Suggested Citation

  • Harsh Parikh & Marco Morucci & Vittorio Orlandi & Sudeepa Roy & Cynthia Rudin & Alexander Volfovsky, 2023. "A Double Machine Learning Approach to Combining Experimental and Observational Data," Papers 2307.01449, arXiv.org, revised Oct 2025.
    • Handle: RePEc:arx:papers:2307.01449
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    References listed on IDEAS

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    3. AmirEmad Ghassami & Chang Liu & Alan Yang & David Richardson & Ilya Shpitser & Eric Tchetgen Tchetgen, 2022. "Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects," Papers 2201.10743, arXiv.org, revised Sep 2025.
    4. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
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    6. de Luna Xavier & Johansson Per, 2014. "Testing for the Unconfoundedness Assumption Using an Instrumental Assumption," Journal of Causal Inference, De Gruyter, vol. 2(2), pages 187-199, September.
    7. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
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    Cited by:

    1. Melody Y Huang & Harsh Parikh, 2024. "Towards Generalizing Inferences from Trials to Target Populations," Papers 2402.17042, arXiv.org, revised May 2024.

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