scholarly journals Model averaging in semiparametric estimation of treatment effects

2015 ◽  
Author(s):  
Chris Muris ◽  
Toru Kitagawa
Keyword(s):  
Treatment Effects ◽  
Semiparametric Estimation ◽  
Model Averaging

scholarly journals Semiparametric Estimation of Treatment Effects in Randomized Experiments

2021 ◽  
Author(s):  
Susan Carleton Athey ◽  
Peter Bickel ◽  
Aiyou Chen ◽  
Guido W. Imbens ◽  
Michael Pollmann
Keyword(s):  
Treatment Effects ◽  
Semiparametric Estimation ◽  
Randomized Experiments

Efficient semiparametric estimation for Gini inequality treatment effects

2017 ◽  
Vol 154 ◽  
pp. 96-100 ◽  
Author(s):  
Xiaofeng Lv ◽  
Rui Li ◽  
Zheng Fang
Keyword(s):  
Treatment Effects ◽  
Semiparametric Estimation

scholarly journals Bayesian Specification Curve Analysis

2020 ◽  
Author(s):  
Christoph Semken ◽  
David Rossell
Keyword(s):  
Technology Use ◽  
Treatment Effects ◽  
Bayesian Model Averaging ◽  
Control Variable ◽  
Model Averaging ◽  
Well Being ◽  
Curve Analysis ◽  
Selective Reporting ◽  
Heterogeneous Effects ◽  
Missing Feature

Science suffers from a reproducibility crisis. Specification Curve Analysis (SCA) helps address this crisis by preventing the selective reporting of results and arbitrary data analysis choices. SCA plots the variability (or heterogeneity) of treatment effects against all ‘reasonable specifications’ (ways to conduct analysis). However, SCA has also been used for formal statistical inference on a type of global average (median) treatment effect (ATE), leading a study by Orben & Przybylski to conclude that ‘the association of [adolescent mental] well-being with regularly eating potatoes was nearly as negative as the association with technology use.’ In contrast, we find relevant associations between certain technologies and well-being, and sharp discrepancies between parent and teenager assessments. These heterogeneous effects are masked by taking medians. In layman’s terms, an ATE may appear practically irrelevant due to averaging over apples and oranges. In addition, the SCA median can have large bias and variance, due to over-weighting statistically implausible control variable specifications. With the Bayesian Specification Curve Analysis (BSCA) we extend SCA to estimate both individual and, if desired, average treatment effects, with controls weighted via Bayesian Model Averaging. The strategy allows to test individual effects, a missing feature in SCA, while improving statistical properties and protecting against false positives. We provide R code that implements BSCA and reproduces our analyses.

scholarly journals Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges

2017 ◽  
Vol 107 (5) ◽  
pp. 278-281 ◽  
Author(s):  
Susan Athey ◽  
Guido Imbens ◽  
Thai Pham ◽  
Stefan Wager
Keyword(s):  
Treatment Effects ◽  
Semiparametric Estimation ◽  
Fixed Number ◽  
Standard Errors ◽  
Treatment Assignment ◽  
Average Treatment ◽  
Point Estimates ◽  
Average Treatment Effects

There is a large literature on semiparametric estimation of average treatment effects under unconfounded treatment assignment in settings with a fixed number of covariates. More recently attention has focused on settings with a large number of covariates. In this paper we extend lessons from the earlier literature to this new setting. We propose that in addition to reporting point estimates and standard errors, researchers report results from a number of supplementary analyses to assist in assessing the credibility of their estimates.

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