[Pre_Learning] Causal Inference in Data Science
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
Topic 1: Introduction to Causal Inference
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LO1: Define key concepts in causal inference, including causality, association, and treatment effects
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LO2: Explain the importance of causal inference in data science and decision-making across various domains
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LO3: Describe the differences between predictive analytics and causal analysis using real-world examples
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Multiple-Choice Questions
