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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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You may also be interested in the
following articles on heterogenous treatment effects:
Imai, Kosuke and Aaron Strauss. (2011). ``Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-out-the-vote Campaign..'' Political Analysis, Vol. 19, No. 1 (Winter), pp. 1-19. Egami, Naoki, and Kosuke Imai (2015). ``Causal Interaction in High Dimension.'' |
You may be
interested in the following software, which implements the proposed
method: ``FintIt: Finding
Heterogeneous Treatment Effects.'' available through
The Comprehensive R
Archive Network. 2015. |