Counterfactuals for the Future


  • Lucius E. J. Bynum New York University
  • Joshua R. Loftus London School of Economics
  • Julia Stoyanovich New York University





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.




How to Cite

Bynum, L. E. J., Loftus, J. R., & Stoyanovich, J. (2023). Counterfactuals for the Future. Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14144-14152.



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