Estimating endogenous treatment effects using latent factor models with and without instrumental variables

Artikel

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 9 ; Year: 2021 ; Issue: 1 ; Pages: 1-25 ; Basel: MDPI

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Health Behavior ; Labor Force and Employment, Size, and Structure
• Thema: disability ; instrumental variable ; latent factor models ; mental illness ; treatment effect

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Banerjee, Souvik ; Basu, Anirban
• Ereignis: Veröffentlichung
• (wer): MDPI
• (wo): Basel
• (wann): 2021

Weitere Informationen

• DOI: doi:10.3390/econometrics9010014
• Handle: http://hdl.handle.net/10419/247604
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ