https://github.com/athammad/onlinecml
Online Causal Machine Learning in Python — one observation at a time
https://github.com/athammad/onlinecml
a-b-testing causal-inference causal-machine-learning econometrics heterogeneous-effects incremental-learning machine-learning-algorithms online-learning-algorithms python
Last synced: about 1 month ago
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Online Causal Machine Learning in Python — one observation at a time
- Host: GitHub
- URL: https://github.com/athammad/onlinecml
- Owner: athammad
- Created: 2026-04-11T05:45:49.000Z (about 2 months ago)
- Default Branch: master
- Last Pushed: 2026-04-11T06:05:07.000Z (about 2 months ago)
- Last Synced: 2026-04-11T07:24:24.686Z (about 2 months ago)
- Topics: a-b-testing, causal-inference, causal-machine-learning, econometrics, heterogeneous-effects, incremental-learning, machine-learning-algorithms, online-learning-algorithms, python
- Language: Python
- Homepage: https://athammad.github.io/onlinecml
- Size: 3.91 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing/adding_estimator.md
Awesome Lists containing this project
README
*Causal inference for the real world — one observation at a time.*
[](https://github.com/athammad/onlinecml/actions/workflows/ci.yml)
\
[](https://github.com/athammad/onlinecml)
[](https://athammad.github.io/onlinecml/)
## Why OnlineCML?
Every major causal inference library (EconML, CausalML, DoWhy) requires a
complete dataset before you begin. But many real-world applications don't
have that luxury:
- A/B tests where you want decisions *now*, not after 30 days
- Clinical trials where treatment effects must be monitored continuously
- Marketing systems where customer data arrives as a stream
- Any setting where the treatment effect might shift over time
OnlineCML processes one observation at a time. No batches. No waiting.
## Installation
```bash
pip install onlinecml
```
## Quickstart
```python
from onlinecml.datasets import LinearCausalStream
from onlinecml.reweighting import OnlineIPW
estimator = OnlineIPW()
for x, treatment, outcome, _ in LinearCausalStream(n=1000, true_ate=2.0, seed=42):
estimator.learn_one(x, treatment, outcome)
print(f"ATE: {estimator.predict_ate():.3f}") # → ~2.0
print(f"95%CI: {estimator.predict_ci()}")
```
## Methods
| Method | Class | ATE | Individual CATE | Doubly Robust |
|---|---|:---:|:---:|:---:|
| Inverse Probability Weighting | `OnlineIPW` | ✓ | — | — |
| Augmented IPW | `OnlineAIPW` | ✓ | ✓ | ✓ |
| Overlap Weights | `OnlineOverlapWeights` | ✓ | — | — |
| S-Learner | `OnlineSLearner` | ✓ | ✓ | — |
| T-Learner | `OnlineTLearner` | ✓ | ✓ | — |
| X-Learner | `OnlineXLearner` | ✓ | ✓ | — |
| R-Learner | `OnlineRLearner` | ✓ | ✓ | — |
| Online Matching | `OnlineMatching` | ✓ | ✓ | — |
| Caliper Matching | `OnlineCaliperMatching` | ✓ | ✓ | — |
| Causal Hoeffding Tree | `CausalHoeffdingTree` | ✓ | ✓ | ✓ |
| Online Causal Forest | `OnlineCausalForest` | ✓ | ✓ | ✓ |
**Novel contributions:** `CausalHoeffdingTree` and `OnlineCausalForest` implement a
custom causal split criterion that maximises between-child CATE variance rather than
outcome MSE, with linear leaf models, doubly robust correction, multi-threshold split
search, and per-tree ADWIN drift detection.
**Policies:** `EpsilonGreedy`, `ThompsonSampling`, `UCB`
**Diagnostics:** `OnlineSMD`, `ATETracker` (with convergence plot and forgetting factor),
`OverlapChecker`, `ConceptDriftMonitor`
**Datasets:** `LinearCausalStream`, `HeterogeneousCausalStream`, `DriftingCausalStream`,
`UnbalancedCausalStream`, `ContinuousTreatmentStream`
**Evaluation:** `progressive_causal_score`, `PEHE`, `ATEError`, `UpliftAUC`, `QiniCoefficient`
## How it differs from batch libraries
| | DoWhy | EconML | CausalML | **OnlineCML** |
|---|:---:|:---:|:---:|:---:|
| Online / streaming | ✗ | ✗ | ✗ | **✓** |
| One-obs-at-a-time | ✗ | ✗ | ✗ | **✓** |
| Concept drift | ✗ | ✗ | ✗ | **✓** |
| Exploration policy | ✗ | ✗ | ✗ | **✓** |
| River compatible | ✗ | ✗ | ✗ | **✓** |
| Online causal forest | ✗ | ✗ | ✗ | **✓** |
| IPW / DR / Overlap | ✓ | ✓ | ✓ | **✓** |
| Meta-learners | ✓ | ✓ | ✓ | **✓** |
| CATE estimation | ✓ | ✓ | ✓ | **✓** |
## Documentation
Full documentation and example notebooks at [athammad.github.io/onlinecml](https://athammad.github.io/onlinecml/).
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md). All PRs require unit tests and
must maintain >90% coverage.
## Citation
```bibtex
@software{onlinecml2025,
title = {OnlineCML: Online Causal Machine Learning in Python},
author = {Hammad, Ahmed},
year = {2025},
url = {https://github.com/athammad/onlinecml}
}
```
## License
MIT