  When evaluating the efficacy of social programs and medical treatments using
randomized experiments, the estimated overall average causal effect alone is
often of limited value and the researchers must investigate when the treatments
do and do not work. Indeed, the estimation of treatment effect heterogeneity
plays an essential role in (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
estimation of heterogeneous treatment effects as a variable selection problem.
We 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. The proposed method is motivated
by and applied to two well-known randomized evaluation studies in the social
sciences. Our method selects the most effective voter mobilization strategies
from a large number of alternative strategies, and it also identifies the
characteristics of workers who greatly benefit from (or are negatively affected
by) a job training program. In our simulation studies, we find that the
proposed method often outperforms some commonly used alternatives.

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