Russian roulette: You can have a deterministic potential-outcome framework, or an asymmetric utility function, but not both
SUTVA can't comfortably handle noisy outcomes.
It has been proposed in medical decision analysis to express the “first do no harm” principle as an asymmetric utility function in which the loss from killing a patient would count more than the gain from saving a life. Such a utility depends on unrealized potential outcomes, and we show how this yields a paradoxical decision recommendation in a simple hypothetical example involving games of Russian roulette. The problem is resolved if we allow the potential outcomes to be random variables. This leads us to conclude that, if you are interested in this sort of asymmetric utility function, you need to move to the stochastic potential outcome framework. We discuss the implications of the choice of parameterization in this setting.
I like this paper! Working out the example and writing it up helped me understand a bunch of things that had puzzled me regarding causal modeling and inference.
Jonas and I engaged on this project after hearing from Amanda Kowalski about her recent paper with Neil Christy, which got us thinking about what you can get from stochastic models for potential outcomes.
There are also useful discussions in the comments to my post on this paper.
Here was my earlier stab at the general problem, back in 2021.