The heterogeneous treatment effect estimation procedure
proposed by Imai and Ratkovic
(2013). The proposed method is
applicable, for example, when selecting a small number of
most (or least) efficacious treatments from a large number
of alternative treatments as well as when identifying
subsets of the population who benefit (or are harmed by) a
treatment of interest. The method adapts the Support Vector
Machine classifier by placing separate LASSO constraints
over the pre-treatment parameters and causal heterogeneity
parameters of interest. This allows for the qualitative
distinction between causal and other parameters, thereby
making the variable selection suitable for the exploration
of causal heterogeneity. The package also contains a class
of functions, CausalANOVA, which estimates the average
marginal interaction effects (AMIEs) by a regularized ANOVA
as proposed by Egami and Imai (2019). It contains a variety
of regularization techniques to facilitate analysis of large
factorial experiments.