I was trying to find out what rubs me the wrong way in this article, and I think it is this: Counterfactuals require a causal model. More so, they belong to at

I was trying to find out what rubs me the wrong way in this article, and I think it is this: Counterfactuals require a causal model. More so, they belong to at least one causal model. This can be a mental model, a formal one, a precise one or a diffuse one. Nevertheless, counterfactuals "come" with an implied model. People may disagree about which model they talk about, and which model is correct. Such a model may or may not be "precise" enough to identify useful parameters, which is the real rub here. For example, the three "Counterfactuals" the author presents presume a model where purging the cache (etc.) directly causes the problem to go away. Each of those statements might come from the same or from a different causal model. The problem, which I feel make this approach not useful, is that the author has no model in mind. In other words, they switch between different models. One counterfactual is, for instance, "If we don't live on earth, then we don't care about the cluster". Let's ignore that the outcome variable is different. The model with "We are not even alive" as a variable is not a reasonable one. First, it is empirically wrong. Second, it does not "identify" parameters (relationships) of interest. These two points are what we strive for when using a model (which we do when we construct counterfactual, even if it is a verbal model!) The author posits that one can add unlimited counterfactuals. What the author means is that one can add unlimited variables and relationships (however diffuse) to the model. This is true. However, we still need to judge whether the model is good, whether it is helpful. In drawing such analogies to statistics, one should not forget the other concepts. From a statistical standpoint, the author begins with a model that does not identify causal parameters associated with the failure to start the cluster. They then add "counterfactuals", that is, they expand their mental model to new causes. They find that any of these variables may cause a counterfactual where the cluster starts. In my view, the author is only half right. Counterfactuals, as presented here, imply a causal model (perhaps not a precise one). Counterfactuals can be useful, if the underlying model is useful. Whether this is the case, depends largely on factors the author does not discuss in the article. Edit: It may also be helpful to distinguish a counterfactual from "observations with exogenous variation". For example, if we purged the cache some time ago purely by accident, and the cluster did start, we might take this as a hint that purging the cache allows the cluster to run. This is not a counterfactual. It can be used to identify causal chains. If we were sure that nothing else was different (in a certain statistical sense), then we could "identify" a causal path from cache purging to staring the cluster. However, to determine that, we again need a model. In fact, we need a model and assumptions about the observation (what is called data generating process) jointly - because really, they are the same thing. For example, if the cache was purged because an engineer discovered an error somewhere, and then did some other action Y - perhaps it was not the purging of the cache that allowed the cluster to run. It may have been action Y. Here, purging the cache was not "exogenous" enough for us to infer the correct reason. Identification of a model/DPG is a deep concept, but a crucial one to finding cause and effect.