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

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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.
Titel på gästpublikation23rd International Conference on Discovery Science
Status!!Accepted/In press - 2020
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemang23rd International Conference in Discovery Science -
Varaktighet: 19 okt 202021 okt 2020


NamnLecture Notes in Computer Science

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Laine, R., Hyttinen, A., & Mathioudakis, M. (Accepterad/under tryckning). Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach. I 23rd International Conference on Discovery Science (Lecture Notes in Computer Science).