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:
Recommendation A/B testing
Interpret experiments with imperfect compliance
Question: A travel website would like to know whether joining a membership program causes users to spend more time engaging with the website.
Problem: They can’t look directly at existing data, comparing members and non-members, because the customers who chose to become members are likely already more engaged than other users. Nor can they run a direct A/B test because they can’t force users to sign up for membership.
Solution: The company had run an earlier experiment to test the value of a new, faster sign-up process. EconML’s DRIV estimator(opens in new tab) (opens in new tab) uses this experimental nudge towards membership as an instrument that generates random variation in the likelihood of membership. The DRIV model adjusts for the fact that not every customer who was offered the easier sign-up became a member and returns the effect of membership rather than the effect of receiving the quick sign-up.
Customer segmentation
Estimate individualized responses to incentives
Question: A media subscription service would like to offer targeted discounts through a personalized pricing plan.
Problem: They observe many features of their customers but are not sure which customers will respond most to a lower price.
Solution: EconML’s DML estimator(opens in new tab) (opens in new tab) uses price variations in existing data, along with a rich set of user features, to estimate heterogeneous price sensitivities that vary with multiple customer features. The tree interpreter(opens in new tab) (opens in new tab) provides a presentation-ready summary of the key features that explain the biggest differences in responsiveness to a discount.
Multi-investment attribution
Distinguish the effects of multiple outreach efforts
Question: A startup would like to know the most effective approach for recruiting new customers: price discounts, technical support to ease adoption, or a combination of the two.
Problem: The risk of losing customers makes experiments across outreach efforts too expensive. So far, customers have been offered incentives strategically, for example larger businesses are more likely to get technical support.
Solution: EconML’s Doubly Robust Learner(opens in new tab) (opens in new tab) model jointly estimates the effects of multiple discrete treatments. The model uses flexible functions of observed customer features to filter out confounding correlations in existing data and deliver the causal effect of each effort on revenue.