Semi-parametric estimation of multi-valued treatment effects for the treated: Estimating equations and sandwich estimators

Arbeitspapier

An estimand of interest in empirical studies with observational data is the average treatment effect of a multi-valued treatment in the treated subpopulation. We demonstrate three estimation approaches: outcome regression, inverse probability weighting and inverse probability weighted regression, where the latter estimator holds a so called doubly robust property. Here, we define the estimators in the framework of partial M-estimation and derive corresponding sandwich estimators of their variances. The finite sample properties of the estimators and the proposed variance estimators are evaluated in simulations that reproduce designs from a previous simulation study in the literature of multi-valued treatment effects. The proposed variance estimators are investigated and compared to a bootstrap estimator.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2020:4

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General
• Subject: ATT ; causal inference ; inverse probability weighting ; doubly robust ; weighted ordinary least squares

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Zetterqvist, Johan ; Waernbaum, Ingeborg
• Event: Veröffentlichung
• (who): Institute for Evaluation of Labour Market and Education Policy (IFAU)
• (where): Uppsala
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/227852
• Last update: 10.03.2025, 11:43 AM CET