Position: All Counterfactuals Are Wrong, and Few Are Useful

_Counterfactuals_ — statements about what _might_ have happened under different circumstances — offer a natural way to phrase causal questions and have gained increasing prominence in contemporary machine learning with applications ranging from evaluating medical decisions to identifying discrimination. This position paper argues that, while counterfactuals are powerful philosophical constructs for describing and formalizing human causal reasoning, the computation of _individual_ counterfactual trajectories is riddled with dangers. In many practical scenarios, individualized counterfactuals are not truly useful or even harmful, either because the required assumptions cannot be adequately justified or because counterfactuals are conceptually misaligned with what would actually be informative for the task at hand.