Causal Inference for Python
Project description
CausalInference
CausalInference is a Python implementation of statistical and econometric methods in the field variously known as Causal Inference, Program Evaluation, and 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 most current development version is hosted on GitHub at: https://github.com/laurencium/causalinference
Package source and binary distribution files are available from PyPi: 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
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, and will run provided the necessary dependencies are in place.
On Ubuntu systems, the following commands should take care of all the essential steps if you are starting from scratch:
$ sudo apt-get update $ sudo apt-get install python-pip python-numpy python-scipy $ sudo pip install causalinference
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.
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