grf-labs / grf
Generalized Random Forests
Why this job: works in the areas we cover, but does not say which job it does
What it does
A package for forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 6 months ago
- Stars
- 1,120
- +1 this week
- Licence
- GPL-3.0
- Share-alike terms
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- C++
- 65% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 88% closed
- 69 still open
- Pull requests
- 953 merged
- 0 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics, project record and preview image
Languages
- C++ 65%
- R 34%
- TeX <1%
- CMake <1%
Topics
- causal-inference
- random-forest
- econometrics
- machine-learning
- statistics
- causal-forest
Project record
- JobOther AI tools
- StatusListed
- Commits2,137
- Forks283
- Watching45
- Stars in 28 days+3
- CreatedAug 12, 2016
- First seen by usNov 7, 2026
- CheckedNov 8, 2026