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Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables

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  • Souvik Banerjee

    (Department of Humanities and Social Sciences, Indian Institute of Technology Bombay, Mumbai 400076, India)

  • Anirban Basu

    (The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA 98195, USA)

Abstract

We provide evidence on the least biased ways to identify causal effects in situations where there are multiple outcomes that all depend on the same endogenous regressor and a reasonable but potentially contaminated instrumental variable that is available. Simulations provide suggestive evidence on the complementarity of instrumental variable (IV) and latent factor methods and how this complementarity depends on the number of outcome variables and the degree of contamination in the IV. We apply the causal inference methods to assess the impact of mental illness on work absenteeism and disability, using the National Comorbidity Survey Replication.

Suggested Citation

  • Souvik Banerjee & Anirban Basu, 2021. "Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables," Econometrics, MDPI, vol. 9(1), pages 1-25, March.
    • Handle: RePEc:gam:jecnmx:v:9:y:2021:i:1:p:14-:d:518981
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    References listed on IDEAS

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    2. Tafti, Elena Ashtari, 2023. "Technology, Skills, and Performance: The Case of Robots in Surgery," CINCH Working Paper Series (since 2020) 78746, Duisburg-Essen University Library, DuEPublico.

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