Estimation of bounds on potential outcomes for decision making
Paper i proceeding, 2020

Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often sufficient for the decision maker to have estimates of upper and lower bounds on the potential outcomes of decision alternatives to assess risks and benefits. We show that, in such cases, we can improve sample efficiency by estimating simple functions that bound these outcomes instead of estimating their conditional expectations, which may be complex and hard to estimate. Our analysis highlights a trade-off between the complexity of the learning task and the confidence with which the learned bounds hold. Guided by these findings, we develop an algorithm for learning upper and lower bounds on potential outcomes which optimize an objective function defined by the decision maker, subject to the probability that bounds are violated being small. Using a clinical dataset and a wellknown causality benchmark, we demonstrate that our algorithm outperforms baselines, providing tighter, more reliable bounds.


Maggie Makar

Massachusetts Institute of Technology (MIT)

Fredrik Johansson

Logik och Typer

John Guttag

D. Sontag

Massachusetts Institute of Technology (MIT)

37th International Conference on Machine Learning, ICML 2020

Vol. PartF168147-9 6617-6627
9781713821120 (ISBN)

37th International Conference on Machine Learning, ICML 2020
Virtual, Online, ,



Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik

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