Counterfactuals for the Future

December 07, 2022 Β· Declared Dead Β· πŸ› AAAI Conference on Artificial Intelligence

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Authors Lucius E. J. Bynum, Joshua R. Loftus, Julia Stoyanovich arXiv ID 2212.03974 Category cs.AI: Artificial Intelligence Cross-listed cs.CY, cs.LG, stat.ME, stat.ML Citations 12 Venue AAAI Conference on Artificial Intelligence Last Checked 4 months ago
Abstract
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.
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