I will say that while I think this article doesn’t give a good treatment of counterfactuals - the “I can offer infinite counterfactuals so counterfactuals aren’t useful” argument is a kind of intellectual nihilism I don’t really buy - I think at its core this article is really just arguing that we should pose forward facing solutions when we encounter bugs, rather than explanations. That proposition seems fine to me.
The main idea: Let Y be the set {y, not y} and X be the set {x, not x}. y denotes timing out, and x denotes “Kubernetes not starting the pod”. We have some distribution P(Y=y|X=x) (in this case, the probability of “timing out” (y) given “Kubernetes not starting the pod” x. The counter factual distribution is NOT the same as P(Y= not y | X= not x) (the “probability of not timing out given Kubernetes starting the pod”). The counterfactual is P(Y’=not y| X=x, Y=y, X’=not x), or “the probability of not timing out given that it did originally time out when Kubernetes did not start the pod, and given that this time it did start the pod”.
This aligns roughly with what you are saying above.