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● Recorded Workshop
Bayesian Hierarchical Modeling for Consumer Choice & Behavioral Intervention Experiments
“Empowering Behavioral Economists with Advanced Bayesian Techniques
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About This Course
This workshop teaches researchers in behavioral economics how to apply Bayesian Hierarchical Modeling to improve consumer choice and behavioral intervention experiments. Participants will learn to address noisy data, small sample sizes, and heterogeneous treatment effects using Bayesian techniques like hierarchical logistic regression and BART. With hands-on experience and real-world datasets, attendees will gain the skills to generate more accurate, publication-ready results.
AIM
The aim of this workshop is to teach researchers how to apply Bayesian Hierarchical Modeling to improve experimental analysis in behavioral economics, addressing data challenges and generating accurate, publication-ready results.
Workshop Objectives
- Apply Bayesian Hierarchical Modeling to consumer choice and behavioral intervention experiments.
- Address challenges like noisy data, small sample sizes, and heterogeneous treatment effects.
- Use Bayesian techniques such as hierarchical logistic regression and BART.
- Enhance experimental analysis to improve data accuracy and robustness.
- Generate publication-ready outputs for top journals.
- Gain hands-on experience with real-world datasets for practical insights.
Workshop Structure
Experimental Data Architecture & Priors
- Structuring hierarchical data: Participants will learn how to structure data with participants nested in conditions, which are further nested in sites.
- Eliciting informative priors: Techniques for gathering priors from meta-analyses will be explored, with hands-on practice.
- Handling censored and truncated choice data: Practical exercises will address common issues in behavioral data and methods to handle them effectively.
Hands On:
Bayesian Inference for Behavioral Models
- Implementing hierarchical logistic regression: Participants will learn to apply Bayesian logistic regression using PyMC and Stan.
- Bayesian model averaging: The workshop will cover theory comparison using Bayesian model averaging to identify the best-fitting model for behavioral data.
- Introduction to Bayesian Additive Regression Trees (BART): Attendees will use BART to identify complex behavioral heterogeneity and model non-linearities in the data.
Hands On:
Publication-Ready Bayesian Outputs
- Generating posterior predictive checks and visualization: Practical exercises will focus on model diagnostics, including generating and interpreting posterior predictive checks.
- Creating forest plots: Participants will visualize multi-site experiment results using forest plots, with a focus on clarity for academic publications.
- Exporting LaTeX tables: Techniques for generating LaTeX tables with credible intervals will be covered, preparing participants for submission to top journals like the Quarterly Journal of Economics and American Economic Review.
Hands On:
Who Should Enroll?
- Researchers and academicians in Behavioral Economics, Consumer Choice, or Public Health.
- Ph.D. scholars and graduate students with a background in economics, data science, or statistics.
- Professionals working with experimental data in social sciences, health interventions, or related fields.
- Basic understanding of statistical modeling and data analysis is recommended.
- Familiarity with Python and R programming languages is beneficial but not mandatory.
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