#In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs (also known as path diagrams, causal Bayesian networks or DAGs) are probabilistic graphical models used to encode assumptions about the data-generating process. Causal graphs can be used for communication and for inference.
causalgraphicalmodels is a python module for describing and manipulating Causal Graphical Models and Structural Causal Models. Behind the scenes it is a light wrapper around the python graph library networkx, together with some CGM specific tools.
It is currently in a very early stage of development. All feedback is welcome.
For a quick overview of CausalGraphicalModel, see this example notebook.
pip install causalgraphicalmodels
My understanding of Causality comes mainly from the reading of the follow work:
- Causality, Pearl, 2009, 2nd Editing. (An overview available here)
- A fantastic blog post, If correlation doesn’t imply causation, then what does? from Michael Nielsen
- These lecture notes from Jonas Peters
- The draft of Elements of Causal Inference
- http://mlss.tuebingen.mpg.de/2017/speaker_slides/Causality.pdf