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Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Ahmed M. Alaa Ibrahim · Mihaela van der Schaar

Wed Jul 11 04:30 AM -- 04:50 AM (PDT) @ K1 + K2

Estimating heterogeneous treatment effects fromobservational data is a central problem in manydomains. Because counterfactual data is inaccessible,the problem differs fundamentally fromsupervised learning, and entails a more complexset of modeling choices. Despite a variety of recentlyproposed algorithmic solutions, a principledguideline for building estimators of treatmenteffects using machine learning algorithmsis still lacking. In this paper, we provide such aguideline by characterizing the fundamental limitsof estimating heterogeneous treatment effects,and establishing conditions under which theselimits can be achieved. Our analysis reveals thatthe relative importance of the different aspectsof observational data vary with the sample size.For instance, we show that selection bias mattersonly in small-sample regimes, whereas witha large sample size, the way an algorithm modelsthe control and treated outcomes is what bottlenecksits performance. Guided by our analysis,we build a practical algorithm for estimatingtreatment effects using a non-stationary Gaussianprocesses with doubly-robust hyperparameters.Using a standard semi-synthetic simulationsetup, we show that our algorithm outperformsthe state-of-the-art, and that the behavior of existingalgorithms conforms with our analysis.

Author Information

Ahmed M. Alaa Ibrahim (UCLA)
Mihaela van der Schaar (UCLA)
Mihaela van der Schaar

Professor van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Turing Faculty Fellow at The Alan Turing Institute in London, and Chancellor's Professor at UCLA. She was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), an NSF Career Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. She holds 35 granted USA patents. In 2019, she was identified by National Endowment for Science, Technology and the Arts as the female researcher based in the UK with the most publications in the field of AI. She was also elected as a 2019 "Star in Computer Networking and Communications". Her current research focus is on machine learning, AI and operations research for healthcare and medicine. For more details, see her website: http://www.vanderschaar-lab.com/

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