- DoWhy - making causal inference easy (Microsoft)
- CausalML - suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research (Uber)
- cfrnet - counterfactual regression (doesn't appear to be actively developed)
- Causality - tools for causal analysis using observational (rather than experimental) datasets (doesn't appear to be actively developed)
- Causal Discovery Toolbox - causal inference in graphs and pairwise settings
- CausalDAG - creation, manipulation, and learning of Causal DAGs
- CausalNex - uses Bayesian Networks to combine machine learning and domain expertise for causal reasoning
- Causal Inference - implements various statistical and econometric methods used in the field variously known as Causal Inference, Program Evaluation, or Treatment Effect Analysis
- EconML - estimating heterogeneous treatment effects from observational data via machine learning (Microsoft)
- scikit-uplift - classic approaches for uplift modeling built on top of scikit-learn
- Networkx - for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks
- Awesome Causalit
- Awesome Open Source
- Introduction to Causal Inference by Brady Neal
- An Hoang for the suggestion to use Shields.io