We’re doing some advertising tests with test and control groups very similar to the example in the Google Research Causal Impact publication except we’re doing state tests and not DMA. I just have a couple questions as I’m reporting the analysis:
Is it okay to re-run the model every several days?
a. I noticed the historical point pred and point effect changing each iteration.
b. Pre.period and post.period remain the same except post is extended like so:
( post.period <- c(366, nrow(disp_dat)) )
Is there a minimum or maximum pre.period amount of data for training? What is best practice?
I would LOVE any help, theory explanation, advice, etc!
Here is my R Code:
pre.period <- c(1, 365) # Set the Snap Shots post.period <- c(366, nrow(data)) # So I can continually update when data is loaded in
T_C_Data <- data.frame(test,control) # (Test, Control)
CA_FL_Rev.impact <- CausalImpact(T_C_Data , pre.period, post.period, model.args = list(nseasons = 7, season.duration = 1)) # day-of-week component to data with daily granularity