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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
Visual Causal Flow
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
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.
[Embodied-AI-Survey-2025] Paper List and Resource Repository for Embodied AI
[RSS 2026] Causal video-action world model for generalist robot control
An R package for causal inference in time series
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
Causal Discovery in Python. Learning causality from data.
A ready-to-fork Claude Code template for academic research — papers, data analysis (R, Stata, Python), slides and teaching — with multi-agent review, enforced quality gates and replication protocols. Codex and Antigravity can use it too.
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Corpus legislativo brasileiro em larga escala e modelo Transformer causal treinado do zero em PyTorch puro.
An agent skill for causal teaching figures through predict, try, and explain. Includes rendering kits, reviewed examples, and browser checks.
Random testing library for C#
Open standard for Provenance & Intent Contracts (PIC) in AI agents. Verify intent, provenance, and evidence before high-impact tool calls.
📊 🌐 🧑🏫 Website for graduate-level course on program evaluation and causal inference using R, built with Quarto