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Panel experiments and dynamic causal effects: A finite population perspective

Author

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  • Iavor Bojinov
  • Ashesh Rambachan
  • Neil Shephard

Abstract

In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative effectiveness of alternative treatment paths. For a rich class of dynamic causal effects, we provide a nonparametric estimator that is unbiased over the randomization distribution and derive its finite population limiting distribution as either the sample size or the duration of the experiment increases. We develop two methods for inference: a conservative test for weak null hypotheses and an exact randomization test for sharp null hypotheses. We further analyze the finite population probability limit of linear fixed effects estimators. These commonly‐used estimators do not recover a causally interpretable estimand if there are dynamic causal effects and serial correlation in the assignments, highlighting the value of our proposed estimator.

Suggested Citation

  • Iavor Bojinov & Ashesh Rambachan & Neil Shephard, 2021. "Panel experiments and dynamic causal effects: A finite population perspective," Quantitative Economics, Econometric Society, vol. 12(4), pages 1171-1196, November.
  • Handle: RePEc:wly:quante:v:12:y:2021:i:4:p:1171-1196
    DOI: 10.3982/QE1744
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    Cited by:

    1. Clément de Chaisemartin & Xavier D’Haultfœuille, 2023. "Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: a survey," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 1-30.
    2. de Chaisemartin, Clément & D’Haultfœuille, Xavier, 2023. "Two-way fixed effects and differences-in-differences estimators with several treatments," Journal of Econometrics, Elsevier, vol. 236(2).
    3. Jonathan Colmer & Mary F. Evans & Jay Shimshack, 2023. "Environmental citizen complaints," CEP Discussion Papers dp1903, Centre for Economic Performance, LSE.
    4. Ruonan Xu & Jeffrey M. Wooldridge, 2022. "A Design-Based Approach to Spatial Correlation," Papers 2211.14354, arXiv.org.
    5. Han, Kevin & Basse, Guillaume & Bojinov, Iavor, 2024. "Population interference in panel experiments," Journal of Econometrics, Elsevier, vol. 238(1).
    6. Vinay Singh & Brijesh Nanavati & Arpan Kumar Kar & Agam Gupta, 2023. "How to Maximize Clicks for Display Advertisement in Digital Marketing? A Reinforcement Learning Approach," Information Systems Frontiers, Springer, vol. 25(4), pages 1621-1638, August.
    7. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    8. Evan Munro & David Jones & Jennifer Brennan & Roland Nelet & Vahab Mirrokni & Jean Pouget-Abadie, 2023. "Causal Estimation of User Learning in Personalized Systems," Papers 2306.00485, arXiv.org.
    9. Francesco Ruggieri, 2023. "Dynamic Regression Discontinuity: A Within-Design Approach," Papers 2307.14203, arXiv.org.
    10. Clément de Chaisemartin & Xavier d'Haultfoeuille, 2022. "Not all Differences-in-differences are Equally Compatible with Outcome-based Selection Models," Working Papers hal-03873930, HAL.
    11. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    12. Iavor Bojinov & David Simchi-Levi & Jinglong Zhao, 2023. "Design and Analysis of Switchback Experiments," Management Science, INFORMS, vol. 69(7), pages 3759-3777, July.
    13. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.

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