@inproceedings{6ac5192300b4499baf42bb67b616fad8,
title = "Counterfactuals for the Future",
abstract = "Counterfactuals are often described as {\textquoteleft}retrospective,{\textquoteright} 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.",
author = "Bynum, {Lucius E.J.} and Loftus, {Joshua R.} and Julia Stoyanovich",
note = "Publisher Copyright: Copyright {\textcopyright} 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 37th AAAI Conference on Artificial Intelligence, AAAI 2023 ; Conference date: 07-02-2023 Through 14-02-2023",
year = "2023",
month = jun,
day = "27",
language = "English (US)",
series = "Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023",
publisher = "AAAI press",
pages = "14144--14152",
editor = "Brian Williams and Yiling Chen and Jennifer Neville",
booktitle = "AAAI-23 Special Tracks",
}