scholarly journals Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions

2018 ◽  
Author(s):  
Vira Semenova ◽  
Victor Chernozhukov
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Simultaneous Inference ◽  
Linear Predictor ◽  
Conditional Average ◽  
Average Treatment ◽  
Structural Functions ◽  
Best Linear Predictor

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 Debiased machine learning of conditional average treatment effects and other causal functions

2020 ◽  
Author(s):  
Vira Semenova ◽  
Victor Chernozhukov
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Nuisance Parameter ◽  
Basis Functions ◽  
Structural Function ◽  
Linear Predictor ◽  
Conditional Average ◽  
Average Treatment ◽  
Best Linear Predictor ◽  
Modern Machine

Summary This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning tools. We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate by modern machine learning techniques. We first adjust the signal to make it insensitive (Neyman-orthogonal) with respect to the first-stage regularisation bias. We then project the signal onto a set of basis functions, which grow with sample size, to get the best linear predictor of the structural function. We derive a complete set of results for estimation and simultaneous inference on all parameters of the best linear predictor, conducting inference by Gaussian bootstrap. When the structural function is smooth and the basis is sufficiently rich, our estimation and inference results automatically target this function. When basis functions are group indicators, the best linear predictor reduces to the group average treatment/structural effect, and our inference automatically targets these parameters. We demonstrate our method by estimating uniform confidence bands for the average price elasticity of gasoline demand conditional on income.

Sign in / Sign up

Export Citation Format

Share Document