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IBM Causal Inference Library
A Python package for computational inference of causal effect.
Description
Causal inference analysis allows estimating of the effect of intervention on some outcome from observational data. It deals with the selection bias that is inherent to such data.
This python package allows creating modular causal inference models that internally utilize machine learning models of choice, and can estimate either individual or average outcome given an intervention. The package also provides the means to evaluate the performance of the machine learning models and their predictions.
The machine learning models must comply with scikit-learn's api
and contain fit() and predict() functions.
Categorical models must also implement predict_proba().
Installation
pip install causallib
Usage
In general, the package is imported using the name causallib.
For example, use
from sklearn.linear_model import LogisticRegression
from causallib.estimation import IPW
ipw = IPW(LogisticRegression())
Comprehensive Jupyter Notebooks examples can be found in the examples directory.
Release files for causallib 0.5.0b0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| causallib-0.5.0b0.tar.gz | 92.6 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| causallib-0.5.0b0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 216.5 kB
Release files / causallib-0.5.0b0.tar.gz
| Download URL | causallib-0.5.0b0.tar.gz |
|---|---|
| Size | 92.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / causallib-0.5.0b0-py3-none-any.whl
| Download URL | causallib-0.5.0b0-py3-none-any.whl |
|---|---|
| Size | 123.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.8
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