Nonparametric Tests for Treatment Effect Heterogeneity
Abstract
A large part of the recent literature on program evaluation has focused on estimation
of the average effect of the treatment under assumptions of unconfoundedness or ignorability
following the seminal work by Rubin (1974) and Rosenbaum and Rubin (1983). In many
cases however, researchers are interested in the effects of programs beyond estimates
of the overall average or the average for the subpopulation of treated individuals.
It may be of substantive interest to investigate whether there is any subpopulation
for which a program or treatment has a nonzero average effect, or whether there is
heterogeneity in the effect of the treatment. The hypothesis that the average effect
of the treatment is zero for all subpopulations is also important for researchers
interested in assessing assumptions concerning the selection mechanism. In this paper
we develop two nonparametric tests. The first test is for the null hypothesis that
the treatment has a zero average effect for any subpopulation defined by covariates.
The second test is for the null hypothesis that the average effect conditional on
the covariates is identical for all subpopulations, in other words, that there is
no heterogeneity in average treatment effects by covariates. Sacrificing some generality
by focusing on these two specific null hypotheses we derive tests that are straightforward
to implement.
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https://hdl.handle.net/10161/1998Collections
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