Uplift modeling and causal inference with machine learning algorithms
active 2025-03-28 → 2026-08-04 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 495 days from 2025-03-28 to 2026-08-04. Pushes: 63 total, peak 6 in a day. Pull requests: 47 total, peak 5 in a day. Issues: 19 total, peak 2 in a day. Comments: 58 total, peak 5 in a day. Stars: 259 total, peak 8 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| jeongyoonlee | 134 | 61 | 28 | 19 |
| Copilot | 33 | 0 | 2 | 7 |
| aman-coder03 | 9 | 0 | 2 | 7 |
| paullo0106 | 7 | 0 | 0 | 3 |
| IyarLin | 5 | 0 | 2 | 3 |
| ras44 | 5 | 0 | 0 | 1 |
| CLAassistant | 4 | 0 | 0 | 4 |
| aaelony | 3 | 0 | 1 | 2 |
| PelinV | 3 | 0 | 0 | 0 |
| stasf25 | 3 | 0 | 0 | 1 |
| jbbqqf | 3 | 0 | 1 | 2 |
| zhenyuz0500 | 2 | 1 | 1 | 0 |
| emilioMaddalena | 2 | 0 | 0 | 2 |
| salmanmkc | 2 | 0 | 2 | 0 |
| mohsinm-dev | 2 | 0 | 2 | 0 |
| xrhd | 2 | 0 | 2 | 0 |
| portchester1989 | 2 | 0 | 0 | 0 |
| benglotzer | 1 | 0 | 0 | 0 |
| Harpati | 1 | 0 | 1 | 0 |
| HSJung93 | 1 | 0 | 1 | 0 |
Recent activity
Latest issues, pull requests and releases
- Issue comment#304jeongyoonlee2026-07-03 03:55return_components for R-Learner
- Pull request#919xrhd2026-06-28 21:07
- Pull request#918xrhd2026-06-20 23:31
- Pull request#914HSJung932026-06-17 00:58
- Issue#911jeongyoonlee2026-06-12 23:45Make meta-learners scikit-learn-compliant estimators (inherit BaseEstimator)
- Pull request#909jeongyoonlee2026-06-08 22:31
- Pull request#909jeongyoonlee2026-06-08 21:44
- Issue comment#854jbbqqf2026-05-09 18:44CausalML is not compatible with Scikit-Learn Pipeline
- Issue comment#517jbbqqf2026-05-09 18:44Question - Cross-validation and Impact of overfitting
- Pull request#899jbbqqf2026-05-09 18:41
- Issue comment#855aman-coder032026-05-01 06:48Polars support on CausalML
- Issue comment#886aman-coder032026-04-25 13:04Add post-fit confidence intervals to `BaseTLearner` via `store_bootstraps` and `return_ci`
- Pull request#891jeongyoonlee2026-04-24 03:56
- Issue comment#538jeongyoonlee2026-04-24 03:47Uplift Forest parameters for multiple treatments
- Issue comment#622jeongyoonlee2026-04-24 03:46Can the methods in causalml be trained with incremental training?
- Issue comment#489jeongyoonlee2026-04-24 03:29is there any way to predict without installing casualml?
- Issue comment#537jeongyoonlee2026-04-24 03:20Question re: Uplift Forest calibration
- Issue comment#889Whatsonyourmind2026-04-05 05:05Add bootstrap confidence intervals and p-values to `rate_score()`
- Issue comment#886aman-coder032026-03-27 05:21Add post-fit confidence intervals to `BaseTLearner` via `store_bootstraps` and `return_ci`
- Issue#540jeongyoonlee2026-03-21 05:16Add the Rank-weighted Average Treatment Effect (RATE) metric
- Pull request#887aman-coder032026-03-21 05:16
- Issue comment#887jeongyoonlee2026-03-21 05:05Add Rank-weighted Average Treatment Effect (RATE) metric
- Issue comment#887aman-coder032026-03-14 08:30Add Rank-weighted Average Treatment Effect (RATE) metric
- Issue#594jeongyoonlee2026-03-13 18:55Reduce number of 3rd party packages required for a prediction-only setup
- Issue#561jeongyoonlee2026-03-13 18:46Currently Uplift Tree/Forest methods run into issues when there is missing data. Is imputing the missing data the only solution now?
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 259 stars here means stars gained during the window, not the repo's star count.