The propensity score plays an important role in causal inference with observational data. However, it is well documented that under slight model misspecifications, propensity score estimates based on maximum likelihood can lead to unreliable treatment effect estimators. To address this practical limitation, this article proposes a new framework for estimating propensity scores that mimics randomize control trials (RCT) in settings where only observational data is available. More specifically, given that in RCTs the joint distritbution of covariates are balanced between treated and not-treated groups, we propose to estimate the propensity score by maxizing the covariate distribution balance  . The proposed propensity score estimators, which we call the integrated propensity score (IPS), are data-driven, do not rely on tuning parameters such as bandwidths, admit an asymptotic linear representation, and can be used to estimate many different treatment effect measures in a unified manner . We derive the asymptotic properties of inverse probability weighted estimators for the average, distributional  and quantile treatment effects based on the IPS and illustrate their relative performance via Monte Carlo simulations and three empirical applications. An implementation of the proposed methods is provided in the new packageIPS forR .
This paper proposes new estimators for the propensity score that aim to maximize the covariate distribution balance among different treatment groups . The proposed propensity score estimators, which we call the integrated propensity score (IPS), are data-driven, do not rely on tuning parameters such as bandwidths, admit an asymptotic linear representation, and can be used to estimate different treatment effect parameters under different identifying assumptions, including unconfoundedness and local treatment effects . We derive the asymptotic properties of inverse probability weighted estimators for the average, distributional , and quantile treatment effects based on the IPS and illustrate their finite sample performance via Monte Carlo simulations and two empirical applications .
R
The propensity score plays an important role in causal inference with observational data. However, it is well documented that under slight model misspecifications, propensity score estimates based on maximum likelihood can lead to unreliable treatment effect estimators. To address this practical limitation, this article proposes a new framework for estimating propensity scores that mimics randomize control trials (RCT) in settings where only observational data is available. More specifically, given that in RCTs the joint distritbution of covariates are balanced between treated and not-treated groups, we propose to estimate the propensity score by maxizing the
This paper proposes new estimators for the propensity score that aim to maximize the
A
among different treatment groups
D
many
R
measures in a unified manner
parameters under different identifying assumptions, including unconfoundedness and local treatment effects
A
,
R
relative
finite sample
D
three empirical applications. An implementation of the proposed methods is provided in the new package
D
IPS
D
for
R
R
two empirical applications