scholarly journals Partially pooled propensity score models for average treatment effect estimation with multilevel data

2021 ◽  
Author(s):  
Youjin Lee ◽  
Trang Q. Nguyen ◽  
Elizabeth A. Stuart
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Multilevel Data ◽  
Average Treatment ◽  
Treatment Effect Estimation ◽  
Effect Estimation

scholarly journals Efficiency of Average Treatment Effect Estimation When the True Propensity Is Parametric

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 25
Author(s):  
Kyoo il Kim
Keyword(s):  
Propensity Score ◽  
Average Treatment Effect ◽  
Parametric Estimation ◽  
Efficient Estimation ◽  
Nonparametric Method ◽  
Average Treatment ◽  
Treatment Effect Estimation ◽  
Average Treatment Effects ◽  
Effect Estimation ◽  
Parametric Class

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.

Propensity score specification for optimal estimation of average treatment effect with binary response

2020 ◽  
Vol 29 (12) ◽  
pp. 3623-3640
Author(s):  
John A Craycroft ◽  
Jiapeng Huang ◽  
Maiying Kong
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Propensity Scores ◽  
Optimal Estimation ◽  
Theoretical Explanation ◽  
Average Treatment Effect ◽  
Adaptive Lasso ◽  
Confounding Bias ◽  
Average Treatment ◽  
The Impact

Propensity score methods are commonly used in statistical analyses of observational data to reduce the impact of confounding bias in estimations of average treatment effect. While the propensity score is defined as the conditional probability of a subject being in the treatment group given that subject’s covariates, the most precise estimation of average treatment effect results from specifying the propensity score as a function of true confounders and predictors only. This property has been demonstrated via simulation in multiple prior research articles. However, we have seen no theoretical explanation as to why this should be so. This paper provides that theoretical proof. Furthermore, this paper presents a method for performing the necessary variable selection by means of elastic net regression, and then estimating the propensity scores so as to obtain optimal estimates of average treatment effect. The proposed method is compared against two other recently introduced methods, outcome-adaptive lasso and covariate balancing propensity score. Extensive simulation analyses are employed to determine the circumstances under which each method appears most effective. We applied the proposed methods to examine the effect of pre-cardiac surgery coagulation indicator on mortality based on a linked dataset from a retrospective review of 1390 patient medical records at Jewish Hospital (Louisville, KY) with the Society of Thoracic Surgeons database.

scholarly journals High-dimensional regression adjustments in randomized experiments

2016 ◽  
Vol 113 (45) ◽  
pp. 12673-12678 ◽  
Author(s):  
Stefan Wager ◽  
Wenfei Du ◽  
Jonathan Taylor ◽  
Robert J. Tibshirani
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
High Dimensional ◽  
Randomized Experiments ◽  
Finite Sample ◽  
Simple Method ◽  
Average Treatment ◽  
Population Average ◽  
High Dimensional Regression ◽  
Effect Estimation

We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample–unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks.

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