Counterfactuals for the Future
December 07, 2022 Β· Declared Dead Β· π AAAI Conference on Artificial Intelligence
"No code URL or promise found in abstract"
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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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