Code

R Packages

evaluCATE: Evaluation of CATE Estimates¹ [Website]

_{Quality evaluation of machine learning estimated conditional average treatment effects (CATEs). The quality is assessed by estimating the best linear predictor of the actual CATEs using the estimated CATEs, the sorted group average treatment effects, and the rank-weighted average treatment effects induced by the estimated CATEs.}

ocf: Ordered Correlation Forest [Website] [CRAN]

_{Machine learning estimator specifically optimized for handling ordered non-numeric outcomes. ‘ocf’ provides forest-based estimation of the conditional choice probabilities and the covariates’ marginal effects. Under an “honesty” condition, the estimates are consistent and asymptotically normal and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2023).}

aggTrees: Aggregation Trees [Website] [CRAN]

_{Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. ‘aggTrees’ allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, one for each level of granularity. For each grouping, we obtain point estimation and inference about the group average treatment effects. Please reference the use as Di Francesco (2022).}