Evaluating Heterogeneous Treatment Effects
Overview
Provides various statistical methods for evaluating heterogeneous treatment effects (HTE) in randomized experiments. The package includes tools to estimate uniform confidence bands for estimation of the group average treatment effect sorted by generic machine learning algorithms (GATES). It also provides the tools to identify a subgroup of individuals who are likely to benefit from a treatment the most "exceptional responders" or those who are harmed by it. Detailed reference in Imai and Li (2023) doi:10.48550/arXiv.2310.07973.
- Updated GATE estimation and tests to use the corrected evalITR implementation.
- Fixed
nsimforwarding intest_itr()and the minimum R version.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-26
- ERROR12 OK, 1 ERROR2026-07-27
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
Show 2 earlier changesHide 2 earlier changes
- ERROR11 OK, 1 ERROR2026-04-25
- OK14 OK2026-03-10
Documentation
- Help pages
- 19
- Examples that run
- 100%7 run in a plain check
- Exported functions with examples
- 64%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 37%
- ACIC2016 HTML
Downloads
Dependencies
Installing evalHTE adds 99 packages (54 with compiled code, 71 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Package metadata
- First published
- 2026-01-28
- Total releases
- 3 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 36 KB
Cite
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Run in R for the authors' preferred citation:
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