Robust Counterfactual Decision in Generalized Linear Models

E. Carrizosa, A. Navas-Orozco

In a Generalized Linear Model (GLM), finding a counterfactual decision of a record amounts to finding a vector with maximal outcome at a distance small enough of the record.
In this paper, we extend this problem and address the challenge of building robust counterfactual decisions. Robustness is understood as guaranteeing that the predicted outcome for the counterfactual decision remains sufficiently high when the nominal probability distribution (the empirical distribution for the given training sample) is replaced by a probability distribution with the same support but different frequencies.
Exploiting the structure of the Exponential Family, the problem of finding a robust counterfactual decision is expressed as a biobjective bilevel nonlinear optimization problem, whose structural properties are studied, and a numerical method is proposed.

Keywords: Data-driven decision-making, counterfactual decisions, Generalized Linear Models, robust optimization, interpretable Machine Learning

Análisis Multivariante
June 12, 2025  7:00 PM
MR 1

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