Estimation of Bounds on Potential Outcomes For Decision Making
This work addresses decision-making in fields like healthcare and economics by offering a more efficient method for risk assessment, though it is incremental as it builds on existing causal inference techniques.
The paper tackles the problem of improving sample efficiency in decision-making by estimating bounds on potential outcomes instead of complex conditional expectations, demonstrating that their algorithm provides tighter and more reliable bounds on clinical and causality benchmark datasets.
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 well-known causality benchmark, we demonstrate that our algorithm outperforms baselines, providing tighter, more reliable bounds.