Course Description

This 10-day course covers the fundamentals and advanced methods in causal inference and Bayesian network modeling, focusing on applications using R. Participants will learn how to draw causal conclusions from observational data, construct Bayesian networks, and use R to implement these techniques in real-world datasets.

Learning Outcomes

• Understand the key principles of causal inference and its applications in observational data.
• Learn about Directed Acyclic Graphs (DAGs) and their role in causal modeling.
• Apply causal inference methods using the "causalimpact" and "dagitty" R packages.
• Construct and interpret Bayesian networks for modeling complex relationships.
• Use R to implement and analyze Bayesian networks in various domains.

Course Structure and Content

Day	Topic
Day 1	Introduction to Causal Inference and Causal Models
Day 2	Understanding and Drawing Directed Acyclic Graphs (DAGs)
Day 3	Identification of Causal Relationships in Observational Data
Day 4	Causal Inference Methods: Backdoor Criterion and Instrumental Variables
Day 5	Introduction to Bayesian Networks and Their Applications
Day 6	Bayesian Network Structure Learning and Parameter Estimation
Day 7	Markov Chain Monte Carlo (MCMC) Methods for Bayesian Network Inference
Day 8	Applications of Bayesian Networks in Healthcare and Economics
Day 9	Advanced Causal Inference Techniques: Propensity Score Matching and Mediation Analysis
Day 10	Practical Applications in R: Analyzing Causal Relationships and Bayesian Networks

Teaching and Learning Strategy

• Lectures with theoretical explanations of causal inference and Bayesian networks.
• Hands-on R programming exercises for causal modeling and Bayesian network analysis.
• Real-world examples and case studies to demonstrate applications of causal inference.
• Interactive sessions and group discussions for problem-solving and knowledge sharing.

Assessment Strategy

• Practical exercises in causal inference and Bayesian network construction (40%)
• Quizzes on causal inference concepts and Bayesian network models (20%)
• Final project involving the application of causal inference or Bayesian networks to a real-world dataset (40%)
• Participants scoring 70% or above will receive a Certificate of Completion.

Certification and Career Opportunities

Upon completion, participants will receive a digital and printed certificate from GLOBALSTAT INTELLIGENCE SOLUTIONS. This course is valuable for data scientists, researchers, and analysts working in fields such as economics, healthcare, and policy-making, helping them gain expertise in advanced statistical modeling and causal analysis techniques.

Contact Information

Email: train@globalstatsol.com

Website: https://www.globalstatsol.com

Enrolling

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