I'm working on the topic of causal inference, I use time-series data. I have two scenarios in front of me and I don't understand the difference:
- Given X and Y "time" features. I would like to know whether X, e.g. average income, does it cause Y, e.g. hotel reservations.
- Given X "time" feature and an intervention. I'm curious to see how the intervention affects X. As an example, I publish a new web interface, while I look at the amount of purchases.
Are both causal inferences? What is the difference between them in practice? A good tool for the second is Google Causalimpact. Could you give me examples of the estimation methods in both cases?
Earlier, I used causal inference on cross-section data sets and that was obvious for me, because I could use DoWhy and a kind of matching and scoring-based estimation methods.