Counterfactuals for the Future

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.

Cite

Text

Bynum et al. "Counterfactuals for the Future." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I12.26655

Markdown

[Bynum et al. "Counterfactuals for the Future." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/bynum2023aaai-counterfactuals/) doi:10.1609/AAAI.V37I12.26655

BibTeX

@inproceedings{bynum2023aaai-counterfactuals,
  title     = {{Counterfactuals for the Future}},
  author    = {Bynum, Lucius E. J. and Loftus, Joshua R. and Stoyanovich, Julia},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {14144-14152},
  doi       = {10.1609/AAAI.V37I12.26655},
  url       = {https://mlanthology.org/aaai/2023/bynum2023aaai-counterfactuals/}
}