Quantco/metalearners

Uplift modeling and causal inference with machine learning algorithms

CausalPFN: Amortized Causal Effect Estimation via In-Context Learning

Causal Inference in Python

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.

CSDID

Machine learning based causal inference/uplift in Python

Automated Double Machine Learning (DoubleML + AutoML) pipeline for causal inference — estimate incremental impact, CATE & ROI of promotions, pricing and media.

A Python package for causal inference in quasi-experimental settings