AICYLGMEMLDec 7, 2022

Counterfactuals for the Future

arXiv:2212.03974v112 citationsh-index: 33
Originality Synthesis-oriented
AI Analysis

This work addresses a theoretical issue in causal inference for researchers, but it appears incremental as it builds on existing counterfactual frameworks.

The paper tackles the problem of counterfactuals being retrospective by proposing forward-looking counterfactuals for treatment choice, showing that mismatches between interventional and forward-looking approaches can lead to counterintuitive results.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes