Causal Inference Collection in Python

Note

The package is in the development stage and will be available soon.

causalinf is a module for causal inference in Python. It provides a set of submodules with implementation of different methods for causal inference. They include:

1. Graphical Causal Models (GCM)
   1. Structural Causal Models (SCM)
   2. Causal Bayesian Networds (CBN)
2. Selection on Observables (SoO)
3. Difference-in-Differences (DiD)
4. Instrumental Variables (IV)
5. Regression Discontinuity (RD)
6. Mediation analysis (MA)
7. Matching Methods (MM)

For each method, these functionalities are provided:

1. Assessment of the plausibility of causal identification assumptions
2. Estimation and inference of the causal effects
3. Reporting the summary results (text, LaTeX, etc.)
4. Conducting sensitivity analysis