# CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects

## TL;DR

CRE is an R package that helps find and interpret subgroups where a treatment has different effects compared to the average treatment effect.

## Contribution

The novel contribution is the implementation of Causal Rule Ensemble, an interpretable method for discovering heterogeneous treatment effects using an ensemble-of-trees approach.

## Key findings

- CRE provides multiple variants of the Causal Rule Ensemble algorithm with different IATE estimators.
- The package addresses limitations of single-tree methods by improving subgroup identification stability and heterogeneity exploration.
- CRE supports interpretable discovery of heterogeneous treatment effects through decision rules.

## Abstract

In health and social sciences, it is critically important to identify interpretable subgroups of the study population where a treatment has notable heterogeneity in the causal effects with respect to the average treatment effect (ATE). Several approaches have already been proposed for heterogeneous treatment effect (HTE) discovery, either estimating first the conditional average treatment effect (CATE) and identifying heterogeneous subgroups in a second stage (Bargagli-Stoffi et al., 2020, 2022; Foster et al., 2011; Hahn et al., 2020), either estimating directly these subgroups in a direct data-driven procedure (Nagpal et al., 2020; Wang & Rudin, 2022). Many of these methodologies are decision tree-based methodologies. Tree-based approaches are based on efficient and easily implementable recursive mathematical programming (e.g., HTE maximization), they can be easily tweaked and adapted to different scenarios depending on the research question of interest, and they guarantee a high degree of interpretability—i.e., the degree to which a human can understand the cause of a decision (Lakkaraju et al., 2016). Despite these appealing features, single-tree heterogeneity discovery is characterized by two main limitations: instability in the identification of the subgroups and reduced exploration of the potential heterogeneity. To accommodate these shortcomings, Bargagli-Stoffi et al. (2023) proposed Causal Rule Ensemble, a new method for interpretable HTE characterization in terms of decision rules, via an extensive exploration of heterogeneity patterns by an ensemble-of-trees approach. CRE is an R package providing a flexible implementation of Causal Rule Ensemble. The package allows for multiple variants of Causal Rule Ensemble algorithm, also including different internal individual average treatment effect (IATE) estimators—i.e., AIPW (Robins et al., 1994), Causal Forest (Athey et al., 2019), Causal BART (Hill, 2011), S-Learner (Hill, 2011), T-Learner (Hansotia & Rukstales, 2002), X-Learner (Künzel et al., 2019).