Randomization inference for treatment effect variation

Peng Ding

doi:10.1111/rssb.12124

Randomization inference for treatment effect variation

2015-07-07

80

OA

AI

DOI：10.1111/rssb.12124

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Applied researchers are increasingly interested in whether and how treatment effects vary in randomized evaluations, especially variation that is not explained by observed covariates. We propose a model-free approach for testing for the presence of such unexplained variation. To use this randomization-based approach, we must address the fact that the average treatment effect, which is generally the object of interest in randomized experiments, actually acts as a nuisance parameter in this setting. We explore potential solutions and advocate for a method that guarantees valid tests in finite samples despite this nuisance. We also show how this method readily extends to testing for heterogeneity beyond a given model, which can be useful for assessing the sufficiency of a given scientific theory. We finally apply our method to the National Head Start impact study, which is a large-scale randomized evaluation of a Federal preschool programme, finding that there is indeed significant unexplained treatment effect variation.

Keyword:

Causal inference

Head Start

Heterogeneous treatment effect

Randomization test

Randomization inference for treatment effect variation

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