Bynum, Lucius E.J., Loftus, Joshua R. 
ORCID: 0000-0002-2905-1632 and Stoyanovich, Julia
(2023)
Counterfactuals for the future.
In: Williams, Brian, Chen, Yiling and Neville, Jennifer, (eds.)
AAAI-23 Special Tracks.
Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 (12).
AAAI Press, pp. 14144-14152.
ISBN 9781577358800
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.
| Item Type: | Book Section |
|---|---|
| Official URL: | https://ojs.aaai.org/index.php/AAAI/index |
| Additional Information: | © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). |
| Divisions: | Statistics |
| Date Deposited: | 01 Sep 2023 10:24 |
| Last Modified: | 20 Dec 2024 00:19 |
| URI: | http://eprints.lse.ac.uk/id/eprint/120115 |
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