Counterfactuals for the Future

Lucius E.J. Bynum, Joshua R. Loftus, Julia Stoyanovich

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Counterfactuals are often described as ‘retrospective,’ focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the structure and stability of exogenous variables in the system being modeled — an assumption that is reasonable in many settings where counterfactuals are used. In this work, we consider cases where we might reasonably make a different assumption about exogenous variables; namely, that the exogenous noise terms of each unit do exhibit some unit-specific structure and/or stability. This leads us to a different use of counterfactuals — a forward-looking rather than retrospective counterfactual. We introduce “counterfactual treatment choice,” a type of treatment choice problem that motivates using forward-looking counterfactuals. We then explore how mismatches between interventional versus forward-looking counterfactual approaches to treatment choice, consistent with different assumptions about exogenous noise, can lead to counterintuitive results.

    Original languageEnglish (US)
    Title of host publicationAAAI-23 Special Tracks
    EditorsBrian Williams, Yiling Chen, Jennifer Neville
    PublisherAAAI press
    Number of pages9
    ISBN (Electronic)9781577358800
    StatePublished - Jun 27 2023
    Event37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States
    Duration: Feb 7 2023Feb 14 2023

    Publication series

    NameProceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023


    Conference37th AAAI Conference on Artificial Intelligence, AAAI 2023
    Country/TerritoryUnited States

    ASJC Scopus subject areas

    • Artificial Intelligence

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