EconML
Estimate causal effects with ML
Overview
EconML is a Python package that applies the power of machine learning techniques to estimate individualized causal responses from observational or experimental data. The suite of estimation methods provided in EconML represents the latest advances in causal machine learning. By incorporating individual machine learning steps into interpretable causal models, these methods improve the reliability of what-if predictions and make causal analysis quicker and easier for a broad set of users.
EconML is open source software developed by the ALICE team at Microsoft Research.
Flexible
Allows for flexible model forms that do not impose strong assumptions, including models of heterogenous responses to treatment.
Unified
Broad set of methods representing latest advances in the econometrics and machine learning literature within a unified API.
Familiar Interface
Built on standard Python packages for machine learning and data analysis.
Use Cases
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. Use cases include:
