causalis 0.2.9.dev347

A Python toolkit for causal inference and experimentation

Causalis

Robust causal inference for experiments and observational studies in Python, organized around scenarios (e.g., Classic RCT, CUPED, Unconfoundedness) with a consistent fit() → estimate() workflow.

• 📚 Documentation & notebooks: https://causalis.causalcraft.com/
• 🔎 API reference: https://causalis.causalcraft.com/api-reference

Why Causalis?

Causalis focuses on:

• Scenario-first workflows (you pick the study design; Causalis provides best-practice defaults).
• Guardrails and diagnostics (e.g., SRM checks, balance checks).
• Typed data contracts (CausalData) to fail fast on schema issues.

Installation

Recommended

pip install causalis

Quickstart: Classic RCT (difference in means + inference)

from causalis.dgp import generate_classic_rct_26
from causalis.scenarios.classic_rct import DiffInMeans, check_srm

# Synthetic RCT data as a validated CausalData object
data = generate_classic_rct_26(seed=42, return_causal_data=True)

# Optional: Sample Ratio Mismatch check
srm = check_srm(data, target_allocation={0: 0.5, 1: 0.5}, alpha=1e-3)
print("SRM detected?", srm.is_srm, "p=", srm.p_value, "chi2=", srm.chi2)

# Estimate treatment effect with t-test inference (or bootstrap / conversion_ztest)
result = DiffInMeans().fit(data).estimate(method="ttest", alpha=0.05)
result.summary()

Quickstart: Observational study (Unconfoundedness / DML IRM)

from causalis.scenarios.unconfoundedness.dgp import generate_obs_hte_26
from causalis.scenarios.unconfoundedness import IRM
from causalis.data_contracts import CausalData

causaldata = generate_obs_hte_26(return_causal_data=True, include_oracle=False)

from causalis.scenarios.unconfoundedness import IRM

model = IRM().fit(causaldata)
result = model.estimate(score='ATTE')
result.summary()

Pick your scenario

Classic RCT: randomized assignment (no pre-period metric).

CUPED: randomized assignment with pre-period metric for variance reduction.

Unconfoundedness: observational study adjusting for measured confounders (DML IRM).

See scenario notebooks: https://causalis.causalcraft.com/explore-scenarios

Responsible use / limitations

Causal estimates require identification assumptions (e.g., randomization or unconfoundedness + overlap). Causalis can help with diagnostics, but it cannot guarantee assumptions hold in your data.

Contributing

Contributions are welcome—bug reports, docs fixes, notebooks, and new estimators. Please read CONTRIBUTING.md and follow the Code of Conduct.

Getting help

Questions: GitHub Discussions

Bugs: GitHub Issues (include minimal repro + versions)

License

MIT (see LICENSE).

Acknowledgements

https://github.com/DoubleML/doubleml-for-py .