Code
R Packages
causalQual: Causal Inference for Qualitative Outcomes
'causalQual' provides a suite of tools for estimating causal effects when the outcome of interest is qualitative - i.e., multinomial or ordered. The package implements the framework introduced in Di Francesco and Mellace (2025), shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs.
valiCATE: Validation of CATE Models
Validation of machine learning models for the conditional average treatment effects (CATEs). Models are validated 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
Machine learning estimator specifically optimized for predictive modeling of 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 (2025).
aggTrees: Aggregation Trees
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 (2024).
