scholarly journals A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS

2017 ◽  
Vol 34 (1) ◽  
pp. 112-133 ◽  
Author(s):  
Tymon Słoczyński ◽  
Jeffrey M. Wooldridge
Keyword(s):  
Instrumental Variable ◽  
Exponential Family ◽  
Treatment Effects ◽  
Robust Estimators ◽  
Conditional Mean ◽  
Double Robustness ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Inverse Probability Weighted ◽  
Doubly Robust

In this paper we study doubly robust estimators of various average and quantile treatment effects under unconfoundedness; we also consider an application to a setting with an instrumental variable. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for subpopulation-specific average treatment effects where subpopulations can be based on covariate values in an arbitrary way. Similar to Wooldridge (2007), we then discuss estimation of the conditional mean using quasi-log likelihoods (QLL) from the linear exponential family.

scholarly journals Augmented Inverse Probability Weighting and the Double Robustness Property

2021 ◽  
pp. 0272989X2110271
Author(s):  
Christoph F. Kurz
Keyword(s):  
Estimation Accuracy ◽  
Probability Weighting ◽  
Robust Method ◽  
Robustness Property ◽  
Inverse Probability ◽  
Double Robustness ◽  
Average Treatment Effects ◽  
Inverse Probability Weighted ◽  
Doubly Robust ◽  
Augmented Inverse Probability Weighting

This article discusses the augmented inverse propensity weighted (AIPW) estimator as an estimator for average treatment effects. The AIPW combines both the properties of the regression-based estimator and the inverse probability weighted (IPW) estimator and is therefore a “doubly robust” method in that it requires only either the propensity or outcome model to be correctly specified but not both. Even though this estimator has been known for years, it is rarely used in practice. After explaining the estimator and proving the double robustness property, I conduct a simulation study to compare the AIPW efficiency with IPW and regression under different scenarios of misspecification. In 2 real-world examples, I provide a step-by-step guide on implementing the AIPW estimator in practice. I show that it is an easily usable method that extends the IPW to reduce variability and improve estimation accuracy. [Box: see text]

scholarly journals Finite‐Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness

Econometrica ◽  
2021 ◽  
Vol 89 (3) ◽  
pp. 1141-1177
Author(s):  
Timothy B. Armstrong ◽  
Michal Kolesár
Keyword(s):  
Treatment Effects ◽  
Lipschitz Constant ◽  
Outcome Variable ◽  
Finite Sample ◽  
Treatment Variable ◽  
Conditional Mean ◽  
Average Treatment ◽  
Optimal Estimator ◽  
Average Treatment Effects ◽  
Supported Work

We consider estimation and inference on average treatment effects under unconfoundedness conditional on the realizations of the treatment variable and covariates. Given nonparametric smoothness and/or shape restrictions on the conditional mean of the outcome variable, we derive estimators and confidence intervals (CIs) that are optimal in finite samples when the regression errors are normal with known variance. In contrast to conventional CIs, our CIs use a larger critical value that explicitly takes into account the potential bias of the estimator. When the error distribution is unknown, feasible versions of our CIs are valid asymptotically, even when n ‐inference is not possible due to lack of overlap, or low smoothness of the conditional mean. We also derive the minimum smoothness conditions on the conditional mean that are necessary for n ‐inference. When the conditional mean is restricted to be Lipschitz with a large enough bound on the Lipschitz constant, the optimal estimator reduces to a matching estimator with the number of matches set to one. We illustrate our methods in an application to the National Supported Work Demonstration.

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