onlinecml 1.1.0

Online Causal Machine Learning in Python

Causal inference for the real world — one observation at a time.

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

pip install onlinecml

Quickstart

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.

Contributing

See CONTRIBUTING.md. All PRs require unit tests and must maintain >90% coverage.

Citation

@software{onlinecml2025,
  title  = {OnlineCML: Online Causal Machine Learning in Python},
  author = {Hammad, Ahmed},
  year   = {2025},
  url    = {https://github.com/athammad/onlinecml}
}

License

MIT