causal-inference

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.

Uplift modeling and causal inference with machine learning algorithms

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.

Coz: Causal Profiling

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

Python Toolkit for Causal and Probabilistic Reasoning

An index of algorithms for learning causality with data

A Python library that helps data scientists to infer causation rather than observing correlation.

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

Causal Discovery in Python. Learning causality from data.

A ready-to-fork Claude Code template for academics using LaTeX/Beamer + R. Multi-agent review, quality gates, adversarial QA, and replication protocols.

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

A curated list of causal inference libraries, resources, and applications.

A Python package for causal inference in quasi-experimental settings

Trustworthy AI related projects

Generalized Random Forests

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的 Claude Code/Codex 期刊技能包，从选题、识别策略到表格规范与审稿回复全流程，助你快速发论文。

Causal Inference for the Brave and True的中文翻译版。全部代码基于Python，适用于计量经济学、量化社会学、策略评估等领域。英文版原作者：Matheus Facure

Open-Source Network Management, Monitoring, ITOM, and Security Analytics

A Python package for modular causal inference analysis and model evaluations

DoubleML - Double Machine Learning in Python

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

A curated list of AI tools, libraries, and resources for economics research, teaching, and policy analysis. Maintained by the OpenEcon team.

Python package for causal discovery based on LiNGAM.