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.
Important Links¶
The official website for Causalinference is
The most current development version is hosted on GitHub at
Package source and binary distribution files are available from PyPi at
For an overview of the main features and uses of Causalinference, please refer to
A blog dedicated to providing a more detailed walkthrough of Causalinference and the econometric theory behind it can be found at
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
Dependencies¶
NumPy: 1.8.2 or higher
SciPy: 0.13.3 or higher
Installation¶
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.