Algorithmic Recourse with Missing Values
- URL: http://arxiv.org/abs/2304.14606v2
- Date: Wed, 22 May 2024 15:16:26 GMT
- Title: Algorithmic Recourse with Missing Values
- Authors: Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike,
- Abstract summary: This paper proposes a new framework of algorithmic recourse (AR) that works even in the presence of missing values.
AR aims to provide a recourse action for altering the undesired prediction result given by a classifier.
Experimental results demonstrated the efficacy of our method in the presence of missing values compared to the baselines.
- Score: 11.401006371457436
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper proposes a new framework of algorithmic recourse (AR) that works even in the presence of missing values. AR aims to provide a recourse action for altering the undesired prediction result given by a classifier. Existing AR methods assume that we can access complete information on the features of an input instance. However, we often encounter missing values in a given instance (e.g., due to privacy concerns), and previous studies have not discussed such a practical situation. In this paper, we first empirically and theoretically show the risk that a naive approach with a single imputation technique fails to obtain good actions regarding their validity, cost, and features to be changed. To alleviate this risk, we formulate the task of obtaining a valid and low-cost action for a given incomplete instance by incorporating the idea of multiple imputation. Then, we provide some theoretical analyses of our task and propose a practical solution based on mixed-integer linear optimization. Experimental results demonstrated the efficacy of our method in the presence of missing values compared to the baselines.
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