Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach

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Abstract

We present a Bayesian approach to evaluate AI decision systems using data from past decisions. Our approach addresses two challenges that are typically encountered in such settings and prevent a direct evaluation. First, the data may not have included all factors that affected past decisions. And second, past decisions may have led to unobserved outcomes. This is the case, for example, when a bank decides whether a customer should be granted a loan, and the outcome of interest is whether the customer will repay the loan. In this case, the data includes the outcome (if loan was repaid or not) only for customers who were granted the loan, but not for those who were not. To address these challenges, we formalize the decision making process with a causal model, considering also unobserved features. Based on this model, we compute counterfactuals to impute missing outcomes, which in turn allows us to produce accurate evaluations. As we demonstrate over real and synthetic data, our approach estimates the quality of decisions more accurately and robustly compared to previous methods.
Original languageEnglish
Title of host publicationDiscovery Science : DS 2020
Number of pages16
Place of PublicationCham
PublisherSpringer
Publication date2020
Pages3-18
ISBN (Print)978-3-030-61526-0
ISBN (Electronic)978-3-030-61527-7
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in conference proceedings
EventInternational Conference in Discovery Science - Thessaloniki, Greece
Duration: 19 Oct 202021 Oct 2020
Conference number: 23
https://ds2020.csd.auth.gr/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12323
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fields of Science

  • 113 Computer and information sciences

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