In causal inference, there is a physical gap between the theoretical question you want to answer and the data you actually have. In this episode, we explain why Identification is the only bridge across that gap. We break down why the 'Assumptions' arrow in your diagram is more important than your algorithm, how to operationalize the link between Causal and Statistical Estimands, and why attempting to estimate a number without first building this bridge leads to misinformation masquerading as truth.
Click the link to read the blogpost.
