scholarly journals Doubly robust uniform confidence band for the conditional average treatment effect function

2016 ◽  
Author(s):  
Yoon-Jae Whang ◽  
Ryo Okui ◽  
Sokbae (Simon) Lee
Keyword(s):  
Treatment Effect ◽  
Confidence Band ◽  
Average Treatment Effect ◽  
Conditional Average ◽  
Effect Function ◽  
Average Treatment ◽  
Doubly Robust

scholarly journals Doubly robust uniform confidence band for the conditional average treatment effect function

2017 ◽  
Vol 32 (7) ◽  
pp. 1207-1225 ◽  
Author(s):  
Sokbae Lee ◽  
Ryo Okui ◽  
Yoon-Jae Whang
Keyword(s):  
Treatment Effect ◽  
Confidence Band ◽  
Average Treatment Effect ◽  
Conditional Average ◽  
Effect Function ◽  
Average Treatment ◽  
Doubly Robust

A doubly robust weighting estimator of the average treatment effect on the treated

Stat ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. e205 ◽  
Author(s):  
Erica E. M. Moodie ◽  
Olli Saarela ◽  
David A. Stephens
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Doubly Robust

scholarly journals A doubly robust estimator for the average treatment effect in the context of a mean-reverting measurement error

Biostatistics ◽  
2016 ◽  
Vol 18 (2) ◽  
pp. 325-337 ◽  
Author(s):  
David Lenis ◽  
Cyrus F. Ebnesajjad ◽  
Elizabeth A. Stuart
Keyword(s):  
Measurement Error ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Robust Estimator ◽  
Average Treatment ◽  
Doubly Robust

scholarly journals Generalizability of heterogeneous treatment effect estimates across samples

2018 ◽  
Vol 115 (49) ◽  
pp. 12441-12446 ◽  
Author(s):  
Alexander Coppock ◽  
Thomas J. Leeper ◽  
Kevin J. Mullinix
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Partial Explanation ◽  
Convenience Sample ◽  
Conditional Average ◽  
Replication Studies ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Effect Heterogeneity

The extent to which survey experiments conducted with nonrepresentative convenience samples are generalizable to target populations depends critically on the degree of treatment effect heterogeneity. Recent inquiries have found a strong correspondence between sample average treatment effects estimated in nationally representative experiments and in replication studies conducted with convenience samples. We consider here two possible explanations: low levels of effect heterogeneity or high levels of effect heterogeneity that are unrelated to selection into the convenience sample. We analyze subgroup conditional average treatment effects using 27 original–replication study pairs (encompassing 101,745 individual survey responses) to assess the extent to which subgroup effect estimates generalize. While there are exceptions, the overwhelming pattern that emerges is one of treatment effect homogeneity, providing a partial explanation for strong correspondence across both unconditional and conditional average treatment effect estimates.

scholarly journals Doubly robust nonparametric inference on the average treatment effect

Biometrika ◽  
2017 ◽  
Vol 104 (4) ◽  
pp. 863-880 ◽  
Author(s):  
D Benkeser ◽  
M Carone ◽  
M J Van Der Laan ◽  
P B Gilbert
Keyword(s):  
Treatment Effect ◽  
Theoretical Study ◽  
Average Treatment Effect ◽  
Robust Inference ◽  
Nuisance Parameters ◽  
Robust Estimators ◽  
Average Treatment ◽  
Adaptive Estimators ◽  
Data Adaptive ◽  
Doubly Robust

Summary Doubly robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double robustness does not readily extend to inference. We present a general theoretical study of the behaviour of doubly robust estimators of an average treatment effect when one of the nuisance parameters is inconsistently estimated. We contrast different methods for constructing such estimators and investigate the extent to which they may be modified to also allow doubly robust inference. We find that while targeted minimum loss-based estimation can be used to solve this problem very naturally, common alternative frameworks appear to be inappropriate for this purpose. We provide a theoretical study and a numerical evaluation of the alternatives considered. Our simulations highlight the need for and usefulness of these approaches in practice, while our theoretical developments have broad implications for the construction of estimators that permit doubly robust inference in other problems.

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