py-why/EconML

Uplift modeling and causal inference with machine learning algorithms

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.

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

Fast and Accurate ML in 3 Lines of Code

⚡ TabPFN: Foundation Model for Tabular Data ⚡

An index of algorithms for learning causality with data

Probabilistic reasoning and statistical analysis in TensorFlow

Automated Machine Learning with scikit-learn

LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence https://arxiv.org/abs/2609.17488