DAS 713 – Causal Inference in Data Science

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About Course

  • Understand the principles of causal inference and the challenges of identifying causal relationships using observational data.
  • Apply causal inference techniques such as propensity score matching, instrumental variables, and regression discontinuity designs to analyze complex datasets.
  • Evaluate and interpret causal effects in real-world applications across healthcare, economics, and social sciences to support evidence-based decision-making and policy development.

Course Content

Week 1: Introduction to Causal Inference

  • Introduction to Causal Inference
    03:54
  • LO1: Define key concepts in causal inference, including causality, association, and treatment effects
  • LO2: Explain the importance of causal inference in data science and decision-making across various domains
  • LO3: Describe the differences between predictive analytics and causal analysis using real-world examples
  • Multiple-Choice Questions
  • True/False Questions
  • Scenario-Based Multiple Choice Questions
  • Key Terms and Concepts Questions
  • Short Answer Questions

Week 2: The Counterfactual Framework

Week 3: Propensity Score Matching

Week 4: Instrumental Variables (IV)

Week 5: Advanced Instrumental Variables Techniques

Week 6: Regression Discontinuity Design (RDD)

Week 7: Applications in Healthcare

Week 8: Midterm Exam or Assignment

Week 9: Applications in Economics

Week 10: Applications in Social Sciences

Week 11: Addressing Challenges in Causal Inference

Week 12: Sensitivity Analysis in Causal Inference

Week 13: Ethical Considerations in Causal Inference

Week 14: Future Trends in Causal Inference

Week 15: Course Review

Week 16: Final Test or Project

Florida Coastal University

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