Efficiency of average treatment effect estimation when the true propensity is parametric

Artikel

It is well known that efficient estimation of average treatment effects can be obtained by the method of inverse propensity score weighting, using the estimated propensity score, even when the true one is known. When the true propensity score is unknown but parametric, it is conjectured from the literature that we still need nonparametric propensity score estimation to achieve the efficiency. We formalize this argument and further identify the source of the efficiency loss arising from parametric estimation of the propensity score. We also provide an intuition of why this overfitting is necessary. Our finding suggests that, even when we know that the true propensity score belongs to a parametric class, we still need to estimate the propensity score by a nonparametric method in applications.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 7 ; Year: 2019 ; Issue: 2 ; Pages: 1-13 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Methodological Issues: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
• Subject: average treatment effect ; efficiency bound ; propensity score ; sieve MLE

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Kim, Kyoo Il
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2019

Further information

• DOI: doi:10.3390/econometrics7020025
• Handle: http://hdl.handle.net/10419/247525
• Last update: 10.03.2025, 11:45 AM CET