Data Science, Analytics, Causal Inference, Editors Pick.
rest on a number of important assumptions (e.g., no important confounders are missing in the data) and there’s no guarantee that what you have found is a true causal effect; judgement and care is needed to assess what the numbers mean. Nevertheless, thinking of potential confounders and how you