causalml

library0.16.0pypypiunverified

CausalML is a Python package for uplift modeling and causal inference with machine learning algorithms. It provides a variety of methods for causal inference in both experimental and observational settings, including meta-learners (S-Learner, T-Learner, X-Learner, R-Learner), tree-based methods (Causal Forest, Uplift Random Forest), and deep learning models. Current version: 0.16.0. Release cadence: irregular, with major updates approximately annually.

ai-ml data llm-agents

pip install causalml

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · CAUSALML

causalml

ai-mlpythonv0.16.0

Install

37.0s avg

Import

—

Disk

1459MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.15.5 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 36.6s

py 3.11

✕ build_error

✓ 35.05s

py 3.12

✕ build_error

✓ 35.6s

py 3.13

✕ build_error

py 3.9

✕ build_error

✓ 40.7s

1459MB installed

● package 1459MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

SLearner

✓ from causalml.inference.meta import SLearner

✗ from causalml.meta import SLearner

Since v0.12, meta-learners moved to causalml.inference.meta

CausalForest

✓ from causalml.inference.forest import CausalForest

✗ from causalml.forest import CausalForest

In v0.14, forest module moved to causalml.inference.forest

set_rfub

✓ from causalml.propensity import set_rfub

✗ from causalml.ufb import set_rfub

Propensity score utilities moved to causalml.propensity in v0.13

Generate synthetic data and train a simple meta-learner to estimate uplift.

import pandas as pd import numpy as np from causalml.dataset import make_uplift_classification from causalml.inference.meta import LRSLearner # Generate synthetic uplift data np.random.seed(42) df, x_names = make_uplift_classification(n_samples=1000, treatment_name=['treatment', 'control'], y_name='y', random_seed=42) # Train S-learner with logistic regression learner = LRSLearner() learner.fit(df[x_names].values, df['treatment'].values, df['y'].values) # Predict uplift for each sample uplift = learner.predict(df[x_names].values) uplift[:5]

Debug

Known issues

breakingIn v0.14, the causalml.inference.meta module was restructured. BaseLearner subclasses like SLearner, TLearner, XLearner no longer accept string params for model constructors; pass model objects directly.

fix

Use `from sklearn.linear_model import LogisticRegression; SLearner(learner=LogisticRegression())` instead of `SLearner(learner='lr')`

affects: >=0.14

breakingThe `causalml.inference.tree` module was removed in v0.15. Uplift tree models are now in `causalml.inference.forest` or should be imported from the forest module.

fix

Use `from causalml.inference.forest import UpliftRandomForestClassifier` instead of any import from `causalml.inference.tree`

affects: >=0.15

deprecatedThe function `causalml.dataset.synthetic_data` is deprecated in favor of `causalml.dataset.make_uplift_classification`.

fix

Replace `from causalml.dataset import synthetic_data` with `from causalml.dataset import make_uplift_classification`

affects: >=0.13

gotchaCausalML's feature importance methods (e.g., `plot_importance()`) may produce different scales than sklearn's built-in importance. Interpretation is relative, not absolute.

fix

Use importance values for ranking features only, not for hypothesis testing.

affects: all

gotchaWhen using S-Learner or T-Learner with categorical features, ensure you one-hot encode them first. The underlying models may not handle cat codes correctly.

fix

Use `pandas.get_dummies()` or `sklearn.preprocessing.OneHotEncoder` before fitting.

affects: all

deprecatedThe `causalml.inference.meta.USM` (Uplift S-Model) class is deprecated as of v0.16. Use `SClassifier` or `SRegressor` instead.

fix

Replace `from causalml.inference.meta import USM` with `from causalml.inference.meta import SClassifier`

affects: >=0.16

Upgrade

Version history

0.16.0latest on PyPI · released Feb 6, 2026

Audit

Dependencies

scikit-learnrequiredCore dependency for model training and evaluation

xgboostrequiredRequired for gradient-boosted tree models

lightgbmoptionalAlternative boosting backend

tensorflowoptionalRequired for neural network models (CEVAE, DragonNet)

Agent activity

29 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/uber/causalml ↗

packagehttps://pypi.org/project/causalml/ ↗

homepagehttps://causalml.readthedocs.io ↗