How terrible of an idea would that be if I just run Logit model and explain the coeffs as the first pass of a causal project?

Tldr: is fitting a Logit model on observational data and explaining the coefficients in the causal manner a terrible idea assuming the X variables aren't too crazy.

So, for context. The goal is to understand what causes store promotions work - which are driven in big part by how the executions go: perfect execution - great result vice versa. The goal is not really to tease out the influence of macro drivers on demand or micro customer level factors either. …

causal context data datascience project variables