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 workshop is to provide a practical framework for better policy assessment and impact analysis. The proposed approach includes Directed Acyclic Graphs and Bayesian Networks. The techniques to be presented in this seminar can help address common challenges regarding causal inference from observational data.
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
November 18, 2014, 3 to 5 pm
George Mason University Arlington Campus
Founders Hall Classroom 118
3301 Fairfax Drive
Arlington, VA 22201-4426
Metro: Virginia Square/GMU
ORANGE LINE 
SILVER LINE 
Paid visitor parking is available in the Founders Hall Garage.
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
We will use recent EPA and NHTSA impact analyses to illustrate the challenges of causal identification and resulting biases.
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 Presenters
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.
Dr. Lionel Jouffe is cofounder and CEO of Bayesia S.A.S., headquartered in Laval, France. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has been working in the field of Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development BayesiaLab, which has since emerged as the leading software package for knowledge discovery, data mining, knowledge modeling, and reasoning using Bayesian networks.
In recent years, Lionel's innovations have substantially helped researchers and analysts improve their analytic workflows, especially with regard to causal analysis.