The R Journal: accepted article

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SortedEffects: Sorted Causal Effects in R PDF download
Shuowen Chen, Victor Chernozhukov, Iván Fernández-Val and Ye Luo

Abstract Chernozhukov et al. (2018) proposed the sorted effect method for nonlinear regression models. This method consists of reporting percentiles of the partial effects, the sorted effects, in addition to the average effect commonly used to summarize the heterogeneity in the partial effects. They also propose to use the sorted effects to carry out classification analysis where the observational units are classified as most and least affected if their partial effect are above or below some tail sorted effects. The R package SortedEffects implements the estimation and inference methods therein and provides tools to visualize the results. This vignette serves as an introduction to the package and displays basic functionality of the functions within.

Received: ; online 2020-03-31, supplementary material, (1.6 Kb)
CRAN packages: SortedEffects, SortedEffect, quantreg, margins, parallel, boot
CRAN Task Views implied by cited CRAN packages: Econometrics, Optimization, SocialSciences, Survival, Environmetrics, ReproducibleResearch, Robust, TimeSeries


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2020-006,
  author = {Shuowen Chen and Victor Chernozhukov and Iván Fernández-Val
          and Ye Luo},
  title = {{SortedEffects: Sorted Causal Effects in R}},
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-006},
  url = {https://journal.r-project.org/archive/2020/RJ-2020-006/index.html}
}