Can you talk about how to infer causality without running an experiment? From your description, "real-time processing + auto ML + algorithm" still sounds very much observational to me.
I'm asking not as knock against your service, but genuine curiosity about how you manage to solve this incredibly hard problem.
EDIT: From your white paper, it looks like you're running a regression that controls for a bunch of confounders. You also interact the treatment variable with those confounders to get the heterogeneous treatment effect.
My concern with that is that we're not controlling for unobservable confounders, which make causal inference so difficult. If we assume that controlling for observable confounders is enough (we shouldn't!), then correlation and causation are the same.
Yep, you're correct that we're using observational studies via a regression to remove confounders and estimate treatment effects. Our confounders are synthetically generated based on the observable variables - we can only make projections of course on digital signals our customers send us (we only use first party data). We are working to incorporate actual experiment data into the algorithm over time as well, to get even closer to the true causal treatment effect.
I'm asking not as knock against your service, but genuine curiosity about how you manage to solve this incredibly hard problem.
EDIT: From your white paper, it looks like you're running a regression that controls for a bunch of confounders. You also interact the treatment variable with those confounders to get the heterogeneous treatment effect.
My concern with that is that we're not controlling for unobservable confounders, which make causal inference so difficult. If we assume that controlling for observable confounders is enough (we shouldn't!), then correlation and causation are the same.
White paper: https://blog.clearbrain.com/posts/introducing-causal-analyti...