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Counterfactual Explanation of Machine Learning Survival Models
Volume 32, Issue 4 (2021), pp. 817–847
Pub. online: 9 December 2021      Type: Research Article      Open accessOpen Access
Received
1 December 2020
Accepted
1 December 2021
Published
9 December 2021

Abstract

A method for counterfactual explanation of machine learning survival models is proposed. One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine learning survival model in the form of survival functions. A condition that establishes the difference between survival functions of the original example and the counterfactual is introduced. This condition is based on using a distance between mean times to event. It is shown that the counterfactual explanation problem can be reduced to a standard convex optimization problem with linear constraints when the explained black-box model is the Cox model. For other black-box models, it is proposed to apply the well-known Particle Swarm Optimization algorithm. Numerical experiments with real and synthetic data demonstrate the proposed method.

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Keywords
interpretable model explainable AI survival analysis censored data convex optimization counterfactual explanation Cox model Particle Swarm Optimization

Funding
This work is supported by the Russian Science Foundation under grant 21-11-00116.

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