Causal Inference Collection in Python

Note

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

causalinf is a package for causal inference in Python. It provides a set of submodules for causal inference using different methods and identification strategies. They include:

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

For each method, a set of core functionalities are provided for:

1. Evaluating the plausibility of causal identification assumptions
2. Estimating causal effects
3. Creating tables and plots with summary results
4. Conducting sensitivity analyses
5. Producing model diagnostics