Cost-Aware Counterfactuals for Black Box Explanations
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
A business user of decision optimization models is always interested in their explanations to identify errors/biases in their formulation and further identify of how their business process can be improved. Explanations for decision optimization can be broadly classified into two paradigms (i) explaining optimal solution in terms of decision variables, (ii) explaining the optimal solution in terms of problem-specific parameters. Both paradigms can be explained by learning interpretable surrogate models, however, we also focus on methods which explain decisions using sensible alternate solution or identify critical constraints in solution.
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
Pavithra Harsha, Ali Koc, et al.
INFORMS 2021
Michael Hersche, Francesco Di Stefano, et al.
NeurIPS 2023
Daniel Karl I. Weidele, Hendrik Strobelt, et al.
SysML 2019