FindIt - Finding Heterogeneous Treatment Effects
The heterogeneous treatment effect estimation procedure
proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.
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)<DOI:10.1080/01621459.2018.1476246>. It contains a
variety of regularization techniques to facilitate analysis of
large factorial experiments.