We consider estimating average treatment effects (ATE) of a binary treatment in observational data when data‐driven variable selection is needed to select relevant covariates from a moderately large number of available covariates . To leverage covariates among predictive of the outcome for efficiency gain while using regularization to fit a parametric propensity score (PS) model, we consider a dimension reduction of based on fitting both working PS and outcome models using adaptive LASSO. A novel PS estimator, the Double‐index Propensity Score (DiPS), is proposed, in which the treatment status is smoothed over the linear predictors for from both the initial working models. The ATE is estimated by using the DiPS in a normalized inverse probability weighting estimator, which is found to maintain double robustness and also local semiparametric efficiency with a fixed number of covariates
The problem of estimating the average treatment effects is important when evaluating the effectiveness of medical treatments or social intervention policies. Most of the existing methods for estimating the average treatment effect rely on some parametric assumptions about the propensity score model or the outcome regression model one way or the other. In reality, both models are prone to misspecification, which can have undue influence on the estimated average treatment effect. We propose an alternative robust approach to estimating the average treatment effect based on observational data in the challenging situation when neither a plausible parametric outcome model nor a reliable parametric propensity score model is available. Our estimator can be considered as a robust extension of the popular class of propensity score weighted estimators. This approach has the advantage of being robust, flexible, data adaptive, and it can handle many covariates simultaneously. Adopting a dimension reduction approach, we estimate the propensity score weights semiparametrically by using a non-parametric link function to relate the treatment assignment indicator to a low-dimensional structure of the covariates which are formed typically by several linear combinations of the covariates. We develop a class of consistent estimators for the average treatment effect and study their theoretical properties. We demonstrate the robust performance of the estimators on simulated data and a real data example of investigating the effect of maternal smoking on babies’ birth weight.
more » « less- Award ID(s):
- 1712706
- NSF-PAR ID:
- 10485804
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 74
- Issue:
- 3
- ISSN:
- 0006-341X
- Format(s):
- Medium: X Size: p. 910-923
- Size(s):
- ["p. 910-923"]
- Sponsoring Org:
- National Science Foundation
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