Bootstrap bias correction for average treatment effects with inverse propensity weights - Archive ouverte HAL Access content directly
Journal Articles Journal of Statistical Research of Iran JSRI Year : 2019

Bootstrap bias correction for average treatment effects with inverse propensity weights

, (1)
1

Abstract

The estimated average treatment effect in observational studies is biased if the assumptions of ignorability and overlap are not satisfied. To deal with this potential problem when propensity score weights are used in the estimation of the treatment effects, in this paper we propose a bootstrap bias correction estimator for the average treatment effect (ATE) obtained with the inverse propensity score (BBC-IPS) estimator. We show in simulations that the BBC-IPC performs well when we have misspecifications of the propensity score (PS) due to: omitted variables (ignorability property may not be satisfied), overlap (imbalances in distribution between treatment and control groups) and confounding effects between observables and unobservables (endogeneity). Further refinements in bias reductions of the ATE estimates in smaller samples are attained by iterating the BBC-IPS estimator.

Dates and versions

hal-03537536 , version 1 (20-01-2022)

Identifiers

Cite

Gubhinder Kundhi, Marcel Voia. Bootstrap bias correction for average treatment effects with inverse propensity weights. Journal of Statistical Research of Iran JSRI, 2019, 52 (2), pp.187-200. ⟨10.47302/jsr.2018520205⟩. ⟨hal-03537536⟩
19 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More