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) <doi:10.2139/ssrn.4304256>.
Version: 2.1.0 Depends: R (≥ 2.10) Imports: boot, broom, car, caret, estimatr, grf, rpart, rpart.plot, stats, stringr Suggests: knitr, rmarkdown Published: 2024-09-09 DOI: 10.32614/CRAN.package.aggTrees Author: Riccardo Di Francesco [aut, cre, cph] Maintainer: Riccardo Di Francesco <difrancesco.riccardo96 at gmail.com> BugReports: https://github.com/riccardo-df/aggTrees/issues License: MIT + file LICENSE URL: https://riccardo-df.github.io/aggTrees/ NeedsCompilation: no Materials: README NEWS CRAN checks: aggTrees results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=aggTrees to link to this page.
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