Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables
Souvik Banerjee () and
Anirban Basu ()
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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
Econometrics, 2021, vol. 9, issue 1, 1-25
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.
Keywords: treatment effect; latent factor models; instrumental variable; mental illness; disability (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:9:y:2021:i:1:p:14-:d:518981
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