English

Counterfactuals for the Future

Artificial Intelligence 2022-12-09 v1 Computers and Society Machine Learning Methodology Machine Learning

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.

Keywords

Cite

@article{arxiv.2212.03974,
  title  = {Counterfactuals for the Future},
  author = {Lucius E. J. Bynum and Joshua R. Loftus and Julia Stoyanovich},
  journal= {arXiv preprint arXiv:2212.03974},
  year   = {2022}
}
R2 v1 2026-06-28T07:25:18.496Z