Book chapter
Counterfactual-Based Synthetic Case Generation
Case-Based Reasoning Research and Development, v 14775, pp 388-403
24 Jun 2024
Abstract
Case augmentation is often desirable when applying case-based reasoning to real-world problems. Initially explored for explainability, counterfactuals were recently recommended as a strategy to augment data. In this work, we implement an existing approach for generating counterfactuals, propose one variant of the original approach, and propose a third approach based on the literature on algorithmic recourse. We apply these three approaches to two datasets in military medical triage. To assess generalization, we also examine one of our approaches on three publicly available datasets. We compare the approaches based on the number of counterfactuals they produce, their resulting accuracy, overlapping counterfactuals, and domain knowledge. Experimental results are encouraging for the proposed approaches and bring up opportunities for future research.
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Details
- Title
- Counterfactual-Based Synthetic Case Generation
- Creators
- Anik SenMallika MainaliChristopher B. RauchUrsula AddisonMichael W. FloydPrateek GoelJustin KarneebRay KulhanekOthalia LarueDavid MénagerMatthew MolineauxJ T TurnerRosina O. Weber
- Publication Details
- Case-Based Reasoning Research and Development, v 14775, pp 388-403
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland; Cham
- Number of pages
- 16
- Grant note
- Defense Advanced Research Projects Agency (DARPA): HR001122S0031
This research was conducted as part of the In the Moment (ITM) project, supported by the Defense Advanced Research Projects Agency (DARPA) under contract number HR001122S0031. We acknowledge other performers from ITM who provided the datasets in the military medical triage. Dataset A was based on data provided by Alyssa Tanaka and colleagues with Soar Technology, LLC. Dataset B was based on work provided by RTX BBN Technologies.
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Information Science (Informatics); College of Computing and Informatics
- Web of Science ID
- WOS:001273511900025
- Scopus ID
- 2-s2.0-85198442416
- Other Identifier
- 991021889478404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods