# CausalInference

The [CausalInference package](https://pypi.org/project/CausalInference/) of Python allows us to generate the values of Adjustment Treatment Effect, Propensity Score, and also enable stratification and improving co-variate balance.

Have a look at the co-lab notebook below to see how the CausalInference package can be used to determine the best Backdoor Adjustment path for a causal model.

**Examples**: [Causal Graphical Models](https://colab.research.google.com/drive/1ZRqQaYWYYuZvtbNIi2EoMSwpcJH-3WJr?usp=sharing)

**Resources**:

1. [CausalInference Documentation](https://github.com/laurencium/causalinference/blob/master/docs/tex/vignette.pdf)
2. [Degenerate State Blog on Backdoor Adjustment](http://www.degeneratestate.org/posts/2018/Jul/10/causal-inference-with-python-part-2-causal-graphical-models/)