Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
This work addresses the need for interpretable causal inference in domains like healthcare or policy, though it appears incremental as it builds on existing matching methods with a focus on hyper-box regions.
The authors tackled the problem of estimating individualized treatment effects from observational data by proposing a matching method that creates unit-specific hyper-box regions in the covariate space, resulting in interpretable and tailored causal effect estimates for each unit.
We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. These regions are large enough that many matches are created for each unit and small enough that the treatment effect is roughly constant throughout. The regions are found as either the solution to a mixed integer program, or using a (fast) approximation algorithm. The result is an interpretable and tailored estimate of a causal effect for each unit.