Causal Analysis and Policy Assessment with Bayesian Networks and BayesiaLab

Causal Analysis and Policy Assessment with Bayesian Networks and BayesiaLab

By Bayesia USA, LLC

Date and time

Tuesday, February 3, 2015 · 3 - 5pm EST

Location

North Carolina Biotechnology Center

15 T W Alexander Dr Dogwood Room Research Triangle Park, NC 27709

Description

Causal Analysis and Policy Assessment with Bayesian Networks and BayesiaLab

A Free 2-Hour Workshop on Causality for Policy Analysts and Researchers

Bayesian Network

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. This approach is meant to help overcome researchers’ common challenges with regard to causality in policy analysis.

Date and Location

February 3, 2015, 3:00pm-5:00pm

North Carolina Biotechnology Center
Dogwood Room
15 TW Alexander Drive
Research Triangle Park, NC 27709

www.ncbiotech.org

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

Potential Outcomes Framework (Neyman-Rubin Model)
Using Directed Acyclic Graphs for Identification (e.g. Back-Door Criterion, etc.)

Simpson's Paradox

Computing the Effect Size Nonparametrically

Using Bayesian Networks and BayesiaLab for Effect Size Computation
Pearl's Graph Mutilation
Jouffe's Likelihood Matching

Managing Uncertainty Probabilistically with Bayesian Networks

Uncertain Evidence
Uncertainty about Policy Implementation (Probabilistic Intervention)
Using BayesiaLab for Optimization under Uncertainty

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.

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