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

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

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。

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

Amortized Inference for Causal Structure Learning, NeurIPS 2022

Operational Database for AI Agents Provide causality lineage, DAG, and State Replay , build to make AI Agents auditable Reliable and EU AI ACT compliant

[ACL2023] Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference

Identifying reasons for human actions in lifestyle vlogs.