09-26, 11:05–11:35 (Europe/Paris), Gaston Berger
Discover metalearners, a cutting-edge Python library designed for Causal Inference with particularly flexible and user-friendly MetaLearner implementations. metalearners leverages the power of conventional Machine Learning estimators and molds them into causal treatment effect estimators. This talk is targeted towards data professionals with some Python and Machine Learning competences, guiding them to optimizing interventions such as 'Which potential customers should receive a voucher to optimally allocate a voucher budget?' or 'Which patients should receive which medical treatment?' based on causal interpretations.
We will introduce metalearners, a particularly flexible and user-friendly MetaLearner implementation for Causal Inference in the Python ecosystem.
Through this session, we aim to demonstrate how metalearners, coupled with standard ML estimators, can produce robust causal models for treatment effects in light of optimizing interventional decision making or policy learning. Unlike existing implementations, it provides a high degree of flexibility: using elaborate pandas and polars features, convenient investigation of base estimators or the ability of reusing pre-trained models are some of its advantages.
Background
In many scenarios in which we intervene with the real world, e.g. administering drugs to patients or distributing vouchers to potential clients, correlation in observational data and prediction models just don't quite do the trick. Conceptually and methodologically, Causal Inference offers the appropriate tools for such scenarios. Yet, practice hasn't quite kept up with the developments on paper. In particular, implementations of MetaLearners - a relatively recent and exciting development in the field of Causal Inference - currently still lack user-friendly, flexible and efficient implementations. metalearners seeks to fill this gap.
Agenda
- Why care about treatment effect estimation? (5')
- Introduction to MetaLearners for treatment effect estimation (5')
- Some shortcomings with existing libraries (5')
- Demo (10')
- Q&A (5')
Target Audience
This talk aims at Data Scientists, ML engineers, AI researchers, and Analysts with some Python familiarity, an understanding of basic Machine Learning concepts and at least some curiosity about causality.
Key Takeaways
- Insights into merging Causal Inference with standard Machine Learning models
- Understanding of metalearners' role in flexible and scalable MetaLearner implementations
Kevin is a Data Scientist at QuantCo, where he's been working with insurances on fraud detection, risk modelling and policy learning. In the past two years he's gotten into the field of Causal Inference. Prior to joining QuantCo, he majored in Computer Science at ETH in Zurich - focusing on Theoretical Computer Science and Machine Learning. He's passionate about Open Source and is the organizer of the PyData Zurich meetup.
Francesc is a MSc Data Science student at ETH Zürich. Before that, he graduated in Maths & CS at the Polytechnic University of Catalonia (UPC) with a year abroad at Columbia University. He joined QuantCo as an intern in February 2024 to work on causal inference and its applications to insurance and fraud detection.