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Evaluating counterfactuals : case studies of modeling choices and their implications Fazelpour, Sina

Abstract

Individuals and organizations often turn to counterfactual questions for actionable insight. The answers provided to such questions can have important consequences, therefore it is critical to examine their reliability. In this dissertation, I demonstrate that examining the reliability of these answers forces us to closely evaluate the choices and assumptions that are embedded in the lifecycle of a counterfactual query -- from its construction to its application. I argue that such an evaluation must proceed in a context-sensitive manner, as it must take into account the intended aim of pursuing counterfactual questions, and also the context wherein the answers to such questions will be used. I highlight the different manners in which our choices and assumptions shape the epistemic and ethical reliability of the answers we provide to counterfactual queries by examining three case studies: (1) the selectivity of imagination-driven counterfactual thought; (2) the use of agent-based models for simulating counterfactuals, specifically in the context of investigating the potential impact of diversity on group performance; and (3) the use of machine learning models for answering counterfactual queries, and the use of counterfactual metrics for assessing the ethical reliability of those models. I argue that making choices and assumptions is inevitable, and that the reliability of our dealings with possibilities depends on being transparent about the influence of these assumptions and choices. Such transparency enhances the epistemic quality of the endeavour wherein counterfactuals are put to use by allowing us to explicitly evaluate when a given choice is justified and why. It also improves our capability to formulate informed mitigation strategies in the face of epistemic and ethical complications.

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Attribution-NonCommercial-NoDerivatives 4.0 International

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