Identification of treatment effect heterogeneity plays an essential
role in a number of situations frequently encountered by applied
researchers. They include (1) selecting the most effective
treatment from a large number of available treatments, (2)
ascertaining subpopulations for which a treatment is effective or
harmful, (3) designing individualized optimal treatment regimes, (4)
testing for the existence or lack of heterogeneous treatment
effects, and (5) generalizing causal effect estimates obtained from
an experimental sample to a target population. In this paper, we
formulate the identification of heterogeneous treatment effects as a
variable selection problem. We then propose a method that adapts the
Support Vector Machine classifier by placing separate sparsity
constraints over the pre-treatment parameters and causal
heterogeneity parameters of interest. To fit the proposed model
with multiple regularization parameters, we develop a
computationally efficient algorithm based on a generalized
cross-validation statistic. Our simulation studies show that the
proposed method tends to yield a lower false discovery rate than
some commonly used alternatives. For empirical illustrations, we
apply the proposed method to well-known randomized field experiments
from the social sciences. (Last revised, December 2011).
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