Causal-Feature-Subset-Selection

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.

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.

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

? Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Evaluation and Tracking for LLM Experiments

A collection of research papers and software related to explainability in graph machine learning.

A library for graph deep learning research

Interpretability and explainability of data and machine learning models

[Embodied-AI-Survey-2024] Paper list and projects for Embodied AI

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