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BayesiaLab

Causality for Policy Assessment and 
Impact Analysis

 

 

Stefan Conrady
Managing Partner, Bayesia USA

Dr. Lionel Jouffe
CEO, Bayesia S.A.S.

Recorded on November 18, 2014, at George Mason University Arlington Campus
Runtime: 01:52:24

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.

This talk is a "live" version of a recent tutorial, Causality for Policy Assessment and 
Impact Analysis - Directed Acyclic Graphs and Bayesian Networks for Causal Identification and Estimation.