SortedEffects: Sorted Causal Effects in R

TL;DR

The paper introduces the SortedEffects R package, which implements methods for estimating, visualizing, and classifying units based on heterogeneous causal effects in nonlinear regression models.

Contribution

It provides an accessible implementation of the sorted effect methodology for analyzing heterogeneity in causal effects, including visualization and classification tools.

Findings

Enables visualization of effect heterogeneity

Allows classification of units based on effect tails

Facilitates interpretation of nonlinear causal effects

Abstract

Chernozhukov et al. (2018) proposed the sorted effect method for nonlinear regression models. This method consists of reporting percentiles of the partial effects in addition to the average commonly used to summarize the heterogeneity in the partial effects. They also proposed to use the sorted effects to carry out classification analysis where the observational units are classified as most and least affected if their causal effects 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.