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Efficient Inference of Average Treatment Effects in High Dimensions via Approximate Residual Balancing

Author

Listed:
  • Athey, Susan

    (Stanford University)

  • Imbens, Guido W.

    (Stanford University)

  • Wager, Stefan

Abstract

There are many studies where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that treatment assignment is as good as random conditional on pre-treatment variables. The unconfoundedness assumption is often more plausible if a large number of pre-treatment variables are included in the analysis, but this can worsen the finite sample properties of existing approaches to estimation. In particular, existing methods do not handle well the case where the model for the propensity score (that is, the model relating pre-treatment variables to treatment assignment) is not sparse. In this paper, we propose a new method for estimating average treatment effects in high dimensions that combines balancing weights and regression adjustments. We show that our estimator achieves the semi-parametric efficiency bound for estimating average treatment effects without requiring any modeling assumptions on the propensity score. The result relies on two key assumptions, namely overlap (that is, all units have a propensity score that is bounded away from 0 and 1), and sparsity of the model relating pre-treatment variables to outcomes.

Suggested Citation

  • Athey, Susan & Imbens, Guido W. & Wager, Stefan, 2016. "Efficient Inference of Average Treatment Effects in High Dimensions via Approximate Residual Balancing," Research Papers 3408, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3408
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    File URL: http://www.gsb.stanford.edu/faculty-research/working-papers/efficient-inference-average-treatment-effects-high-dimensions
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    Citations

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    Cited by:

    1. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    2. Dmitry Arkhangelsky & Guido Imbens, 2018. "Fixed Effects and the Generalized Mundlak Estimator," Papers 1807.02099, arXiv.org, revised Aug 2023.
    3. Soumajyoti Sarkar & Hamidreza Alvari, 2020. "Mitigating Bias in Online Microfinance Platforms: A Case Study on Kiva.org," Papers 2006.12995, arXiv.org.
    4. Susan Athey & Guido Imbens & Thai Pham & Stefan Wager, 2017. "Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges," American Economic Review, American Economic Association, vol. 107(5), pages 278-281, May.
    5. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    6. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    7. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
    8. Matthias Huber & Simone Schüller & Marc Stöckli & Klaus Wohlrabe, 2018. "Machine Learning in Economic Research," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(07), pages 50-53, April.
    9. Dmitry Arkhangelsky & Guido W. Imbens, 2019. "Doubly Robust Identification for Causal Panel Data Models," Papers 1909.09412, arXiv.org, revised Feb 2022.
    10. Thai T. Pham & Yuanyuan Shen, 2017. "A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform," Papers 1706.02795, arXiv.org.

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