A collection of python libraries that are useful for causal machine learning.

General

PyWhy. an organization that holds a multitude of causal machine learning packages in python
DoWhy. A package that offers a high level API for graph definition, identification, estimation, sensitivity analysis and refutation
PGMPY. A package for general Bayesian network definition and inference (not just causal inference)

Identification

Ananke. A package that implements advanced graph based identification algorithms and allows for semi-parametric estimation.
y0. Advanced graph based identification algorithms for general causal graphs (even allowing for un-directed edges)

EconML. Many causal ML algorithms for estimation and confidence intervals for heterogeneous treatment effects (under conditional exogeneity or with instruments)
CausalML. Many causal ml algorithms for estimation of heterogeneous treatment effects
UpliftML. A smaller set of causal ML algorithms for heterogeneous effect estimation, but which scales on Spark, using PySpark
DoubleML. Implements the double ML estimation algorithm with inference, for average treatment effects under exogeneity or with instruments

PyWhy-Graphs. A basic library for causal graph manipulation and basic graph algorithms (e.g. conditional independence)
DoDiscover. A package for causal discovery in python
Causica. A deep learning based causal discovery package
CausalLearn. Causal discovery package with many recent advanced algorithms from the research community