DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Causal discovery made easy.

Causal Modeling with Stationary Diffusions, AISTATS 2024

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

A large pile of interesting and/or useful information

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Fast and customizable framework for automatic and quick Causal Inference in Python

A Python package for causal inference in quasi-experimental settings

This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (particularly, with Nonlinear ICA) can be used to extract the causal graph from an underlying structural equation model (SEM).

Causal machine learning course in Python

Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

An optimization-based algorithm to accurately estimate the causal effects and robustly predict under distribution shifts. It leverages the invariance of causality over multiple environments.

[Experimental] Global causal discovery algorithms

nl-causal: nonlinear causal inference based on IV regression in Python

Python package for causal discovery based on LiNGAM.

[ICLR24] A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error

(Realtime) Temporal Convolutions in PyTorch

Uplift modeling and causal inference with machine learning algorithms

Normalising meteorology on air quality

Code for Conformal Counterfactual Inference under Hidden Confounding (KDD’24)