Machine Learning, Causal Inference, Data Science, Deep Dives, Notes From Industry.
estimating causal effects from observational data is difficult because of confounding. To see what this means, consider (1) product quality and (2) seasons as two important examples of many potential confounders: MacBooks are more expensive than, say, Chromebooks. Assuming a retailer sells more