Estimation of Utility-Maximizing Bounds on Potential Outcomes

Paper in 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.