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.
The official website for Causalinference is
https://causalinferenceinpython.org
The most current development version is hosted on GitHub at
https://github.com/laurencium/causalinference
Package source and binary distribution files are available from PyPi at
https://pypi.python.org/pypi/causalinference
For an overview of the main features and uses of Causalinference, please refer to
https://github.com/laurencium/causalinference/blob/master/docs/tex/vignette.pdf
A blog dedicated to providing a more detailed walkthrough of Causalinference and the econometric theory behind it can be found at
https://laurencewong.com/software/
- 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.
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.