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Poster
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Andrew Jesson · Panagiotis Tigas · Joost van Amersfoort · Andreas Kirsch · Uri Shalit · Yarin Gal

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @
in Poster Session 1 »

Estimating personalized treatment effects from high-dimensional observational data is essential in situations where experimental designs are infeasible, unethical, or expensive. Existing approaches rely on fitting deep models on outcomes observed for treated and control populations. However, when measuring individual outcomes is costly, as is the case of a tumor biopsy, a sample-efficient strategy for acquiring each result is required. Deep Bayesian active learning provides a framework for efficient data acquisition by selecting points with high uncertainty. However, existing methods bias training data acquisition towards regions of non-overlapping support between the treated and control populations. These are not sample-efficient because the treatment effect is not identifiable in such regions. We introduce causal, Bayesian acquisition functions grounded in information theory that bias data acquisition towards regions with overlapping support to maximize sample efficiency for learning personalized treatment effects. We demonstrate the performance of the proposed acquisition strategies on synthetic and semi-synthetic datasets IHDP and CMNIST and their extensions, which aim to simulate common dataset biases and pathologies.

Keywords: Theory · Deep Learning · Causality · Active Learning

Author Information

Andrew Jesson (University of Oxford)
Panagiotis Tigas (University of Oxford)
Joost van Amersfoort (University of Oxford)
Andreas Kirsch (University of Oxford)

AIMS DPhil at University of Oxford with Prof Yarin Gal at OATML DeepMind: performance research engineer for 1 year Google: software engineer for 3 years MSc CompSci, BSc CompSci, BSc Maths at TU Munich

Uri Shalit (Technion)
Yarin Gal (University of Oxford)
Yarin Gal

Yarin leads the Oxford Applied and Theoretical Machine Learning (OATML) group. He is an Associate Professor of Machine Learning at the Computer Science department, University of Oxford. He is also the Tutorial Fellow in Computer Science at Christ Church, Oxford, and a Turing Fellow at the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence. Prior to his move to Oxford he was a Research Fellow in Computer Science at St Catharine’s College at the University of Cambridge. He obtained his PhD from the Cambridge machine learning group, working with Prof Zoubin Ghahramani and funded by the Google Europe Doctoral Fellowship. He made substantial contributions to early work in modern Bayesian deep learning—quantifying uncertainty in deep learning—and developed ML/AI tools that can inform their users when the tools are “guessing at random”. These tools have been deployed widely in industry and academia, with the tools used in medical applications, robotics, computer vision, astronomy, in the sciences, and by NASA. Beyond his academic work, Yarin works with industry on deploying robust ML tools safely and responsibly. He co-chairs the NASA FDL AI committee, and is an advisor with Canadian medical imaging company Imagia, Japanese robotics company Preferred Networks, as well as numerous startups.

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