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Causality for Policy Assessment and Impact Analysis

The objective of this presentation is to provide a practical framework for causal effect estimation with non-experimental data. We will present a range of methods, including Directed Acyclic Graphs and Bayesian networks, which can help distinguish causation from association when working with data from observational studies. The presentation revolves around a seemingly trivial example, Simpson’s Paradox, which turns out to be rather tricky to interpret in practice.