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.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Next generation of automated data exploratory analysis and visualization platform.

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

An index of algorithms for learning causality with data

[Embodied-AI-Survey-2025] Paper List and Resource Repository for Embodied AI

Causal Discovery in Python. Learning causality from data.

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Eliot: the logging system that tells you *why* it happened

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