Unit Selection with Causal Diagram

TL;DR

This paper enhances unit selection strategies by incorporating causal diagrams, allowing for tighter bounds on benefit estimation and improved decision-making using combined observational and experimental data.

Contribution

It demonstrates how causal models can significantly narrow bounds on benefit functions, improving unit selection accuracy over previous methods.

Findings

Causal diagrams improve benefit estimation accuracy.

Structural information narrows bounds enough to influence decisions.

Method combines observational and experimental data effectively.

Abstract

The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if encouraged and a different way if not encouraged. Using a combination of experimental and observational data, Li and Pearl derived tight bounds on the "benefit function" - the payoff/cost associated with selecting an individual with given characteristics. This paper shows that these bounds can be narrowed significantly (enough to change decisions) when structural information is available in the form of a causal model. We address the problem of estimating the benefit function using observational and experimental data when specific graphical criteria are assumed to hold.