> Say I have a simple table of outdoor temperatures and ice cream sales.
You have more than that! You have knowledge about the world!
> What can the machinery of causal inference do for me in this situation?
Well, (I’m being purposefully pedantic here) you haven’t really asked a question yet. The first thing it can do is help you while you’re formulating one. It can answer questions like, “how can I anticipate how things I have and havent measured will the estimates I’m interested in/making?”
> If it doesn’t apply here, what do I need to add to my dataset to make it appropriate for causal inference? More columns of data? Explicit assumptions?
The first thing you need to do is articulate what you’re actually interested in. Then you need to be explicit about the causal relationships between things relevant to those questions. The big thing (to me) is that particular causal structures have testable conditional independence structures and by assessing these, you can build evidence for or against particular diagrams of the context.
You have more than that! You have knowledge about the world!
> What can the machinery of causal inference do for me in this situation?
Well, (I’m being purposefully pedantic here) you haven’t really asked a question yet. The first thing it can do is help you while you’re formulating one. It can answer questions like, “how can I anticipate how things I have and havent measured will the estimates I’m interested in/making?”
> If it doesn’t apply here, what do I need to add to my dataset to make it appropriate for causal inference? More columns of data? Explicit assumptions?
The first thing you need to do is articulate what you’re actually interested in. Then you need to be explicit about the causal relationships between things relevant to those questions. The big thing (to me) is that particular causal structures have testable conditional independence structures and by assessing these, you can build evidence for or against particular diagrams of the context.