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.
pip install dowhy
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.
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins