scholarly journals Covariate selection and model averaging in semiparametric estimation of treatment effects

2013 ◽  
Author(s):  
Toru Kitagawa ◽  
Chris Muris
Keyword(s):  
Treatment Effects ◽  
Semiparametric Estimation ◽  
Model Averaging ◽  
Covariate Selection

scholarly journals Covariate Selection in High-Dimensional Propensity Score Analyses of Treatment Effects in Small Samples

2011 ◽  
Vol 173 (12) ◽  
pp. 1404-1413 ◽  
Author(s):  
Jeremy A. Rassen ◽  
Robert J. Glynn ◽  
M. Alan Brookhart ◽  
Sebastian Schneeweiss
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Small Samples ◽  
High Dimensional ◽  
Covariate Selection

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.

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