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Causal Machine Learning for Real-World Impact
Nick Pawlowski · Jeroen Berrevoets · Caroline Uhler · Kun Zhang · Mihaela van der Schaar · Cheng Zhang

Fri Dec 02 06:30 AM -- 03:00 PM (PST) @ Room 295 - 296
Event URL: https://cml-4-impact.vanderschaar-lab.com »

Causality has a long history, providing it with many principled approaches to identify a causal effect (or even distill cause from effect). However, these approaches are often restricted to very specific situations, requiring very specific assumptions. This contrasts heavily with recent advances in machine learning. Real-world problems aren’t granted the luxury of making strict assumptions, yet still require causal thinking to solve. Armed with the rigor of causality, and the can-do-attitude of machine learning, we believe the time is ripe to start working towards solving real-world problems.

Author Information

Nick Pawlowski (Microsoft Research)
Jeroen Berrevoets (University of Cambridge)
Caroline Uhler (Massachusetts Institute of Technology)
Kun Zhang (CMU & MBZUAI)
Mihaela van der Schaar (University of Cambridge)
Cheng Zhang (Microsoft Research, Cambridge, UK)

Cheng Zhang is a principal researcher at Microsoft Research Cambridge, UK. She leads the Data Efficient Decision Making (Project Azua) team in Microsoft. Before joining Microsoft, she was with the statistical machine learning group of Disney Research Pittsburgh, located at Carnegie Mellon University. She received her Ph.D. from the KTH Royal Institute of Technology. She is interested in advancing machine learning methods, including variational inference, deep generative models, and sequential decision-making under uncertainty; and adapting machine learning to social impactful applications such as education and healthcare. She co-organized the Symposium on Advances in Approximate Bayesian Inference from 2017 to 2019.

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