Finding Heterogeneous Treatment Effects
Estimating the AMEs and AMIEs with the CausalANOVA.
Estimating the Conditional Effects with the CausalANOVA.
Cross validation for the CausalANOVA.
FindIt: Finding Heterogeneous Treatment Effects
FindIt for Estimating Heterogeneous Treatment Effects
Plotting CausalANOVA
Plot estimated treatment effects or predicted outcomes for each treatm...
Computing predicted values for each sample in the data.
Summarizing CausalANOVA output
Summarizing FindIt output
Estimating the AMEs and AMIEs after Regularization with the CausalANOV...
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.