EconML is a Python package that applies the power of machine learning techniques to estimate individualized causal responses from observational or experimental data.

If you are new to causal inference, our guide will introduce you to the biggest challenges in answering causal questions, how EconML addresses those challenges, and the standard causal terminology we …

The econml package works on macOS, Windows, and Linux, and supports Python versions 3.5-3.7. The econml package relies on numpy, scipy, and scikit-learn for most of its underlying numerical …

Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber Vasilis Syrgkanis, Greg Lewis, Miruna Oprescu, Maggie Hei, Keith …

Solution: EconML’s Doubly Robust Learner model jointly estimates the effects of multiple discrete treatments. The model uses flexible functions of observed customer features to filter out confounding …

Frequently asked questions When should I use EconML? What are the advantages of EconML? How do I know if the results make sense? I’m getting causal estimates that make no sense. What next? What …

Open source packages such as CausalML and EconML provide a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for causal inference.

EconML is an open source Python package developed by the ALICE team at Microsoft Research that applies the power of machine learning techniques to estimate individualized causal responses from …

EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft …

Jun 2, 2021 · In the news | Counterfactual Introduction to EconML Packages (meta-learners) April 7, 2019 In recent years, research in the fusion of econometrics and machine learning has been …