Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
By Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky. 2020
16 citations recorded by the source corpus.
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.
Subfields: Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Cynthia Rudin
- Alexander Volfovsky
- Sudeepa Roy
- Harsh Parikh
- Berk Ustun
- Kush R. Varshney
- Nadia Burkart
- Marco F. Huber
Citations in the corpus
- FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “, 2019) or adaptive hyperboxes (AHB) (Morucci et al., 2020), both of which use distance metrics that are different than the adaptive Hamming distance that FLAME uses.”
- MALTS: Matching After Learning to Stretch (2018, background): “A sister work, developed in parallel, is that of Morucci et al. (2020), which learns adaptively-sized hyperboxes as matched groups.”