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Causal Inference in Python

Project description

Causal Inference in Python

Causal Inference in Python, or Causalinference in short, is a software package that implements various statistical and econometric methods used in the field variously known as Causal Inference, Program Evaluation, or Treatment Effect Analysis.

Work on Causalinference started in 2014 by Laurence Wong as a personal side project. It is distributed under the 3-Clause BSD license.

Main Features

  • Assessment of overlap in covariate distributions
  • Estimation of propensity score
  • Improvement of covariate balance through trimming
  • Subclassification on propensity score
  • Estimation of treatment effects via matching, blocking, weighting, and least squares


  • NumPy: 1.8.2 or higher
  • SciPy: 0.13.3 or higher


Causalinference can be installed using pip:

$ pip install causalinference

For help on setting up Pip, NumPy, and SciPy on Macs, check out this excellent guide.

Minimal Example

The following illustrates how to create an instance of CausalModel:

>>> from causalinference import CausalModel
>>> from causalinference.utils import random_data
>>> Y, D, X = random_data()
>>> causal = CausalModel(Y, D, X)

Invoking help on causal at this point should return a comprehensive listing of all the causal analysis tools available in Causalinference.

Project details

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