Causal Analysis and Policy Assessment with Bayesian Networks and BayesiaLab
A Free 2-Hour Workshop on Causality for Policy Analysts and Researchers
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.
Beyond covering the theory of Directed Acyclic Graphs and Bayesian networks, we will show you an entire causal analysis workflow using the BayesiaLab software platform. If you are interested, you will be able to follow this exercise, during the workshop or afterwards, using a trial version of BayesiaLab (click here to download the BayesiaLab trial).
This workshop is a "live" version of a new 63-page BayesiaLab tutorial, Causality for Policy Assessment and Impact Analysis - Directed Acyclic Graphs and Bayesian Networks for Causal Identification and Estimation.
Date and Location
February 3, 2015, 3:00pm-5:00pm
Hamner Conference Center at the
North Carolina Biotechnology Center
Dogwood Room
15 TW Alexander Drive
Research Triangle Park, NC 27709
Our course venue is located just 10 minutes from the Raleigh-Durham International Airport (RDU).
Workshop Overview
What is Policy Analysis?
Causal Inference by Experiment
Causal Inference from Data plus Theory
Causal Effect Identification
Computing the Effect Size Nonparametrically
Jouffe's Likelihood Matching
Managing Uncertainty Probabilistically with Bayesian Networks
Who should attend?
Policy analysts, decision makers, policy consultants, applied researchers, statisticians, social scientists, data scientists, ecologists, epidemiologists, econometricians, economists, market researchers, knowledge managers, students and teachers in related fields.
About the Presenter
Stefan Conrady is the managing partner of Bayesia USA, the North American sales and marketing organization of France-based Bayesia S.A.S.
Stefan studied Electrical Engineering in Ulm, Germany, and has extensive international management experience in the fields of product strategy, marketing, market research, and analytics, all with leading car brands, including Mercedes-Benz, BMW, Rolls-Royce, Nissan, and Infiniti. Most recently, prior to joining Bayesia, Stefan was heading the Analytics & Forecasting group at Nissan North America.
Throughout his assignments in North America, Europe, and Asia, Stefan gained first-hand experience of how Fortune 100 corporations perform impact assessments of strategic initiatives. Thus, he is in a unique position to speak about the real-world practice of policy analysis, which often ignores the important distinction between observational and causal inference.