Machine Learning, Causal Inference, Data Science, Deep Dives, Notes From Industry.
models are both trained independently with a set X of potential confounding variables (e.g. product quality and season), so that their predictions approximate expected values, E[P|X] and E[Q|X]. Using these predictions, I then residualize out the parts of treatment and effect that are predictable