Causal Inference 2
Advanced course exploring mediation, principal stratification, longitudinal analysis, and regression discontinuity in causal inference.
Advanced course exploring mediation, principal stratification, longitudinal analysis, and regression discontinuity in causal inference.
This advanced Master's-level course builds upon foundational causal inference concepts, exploring sophisticated statistical methods for analyzing causal relationships. Led by Professor Michael E. Sobel, the course covers cutting-edge topics including mediation analysis, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models. Students learn to apply these advanced techniques to real-world problems in science, medicine, policy, and business.
3.4
(14 ratings)
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English
English (Original), Deutsch (Auto), हिन्दी (ऑटो), 18 more
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What you'll learn
Master advanced mediation analysis techniques
Understand principal stratification and its applications
Apply longitudinal causal inference methods
Implement regression discontinuity designs
Analyze interference in causal relationships
Use fixed effects models for causal analysis
Skills you'll gain
This course includes:
168 Minutes PreRecorded video
4 assignments
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FullTime access
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There are 5 modules in this course
This advanced course delves into sophisticated topics in causal inference at the Master's level. The curriculum covers complex methodological approaches including mediation analysis, principal stratification, and longitudinal causal inference. Students explore advanced techniques such as regression discontinuity, interference analysis, and fixed effects models. The course emphasizes both theoretical understanding and practical application of these advanced statistical methods in research and analysis.
Module 7: Introduction to Mediation
Module 1 · 1 Hours to complete
Module 8: More on Mediation
Module 2 · 35 Minutes to complete
Module 9: Instrumental Variables, Principal Stratification, and Regression Discontinuity
Module 3 · 44 Minutes to complete
Module 10: Longitudinal Causal Inference
Module 4 · 39 Minutes to complete
Module 11: Interference and Fixed Effects
Module 5 · 2 Hours to complete
Fee Structure
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Instructor
Professor of Statistics at Columbia University
Dr. Michael E. Sobel is a Professor of Statistics at Columbia University, specializing in causal inference. His research encompasses various aspects of this field, including mediation, interference, longitudinal causal inference using fixed effects models, meta-analysis, compliance, and causal inference in fMRI experiments, which involve analyzing extensive time series data collected under different experimental conditions. In addition to advancing his work in fMRI, Dr. Sobel is investigating interference in observational studies and developing new estimands for broader counterfactual inference. He has published numerous influential papers and is recognized for his contributions to statistical methodologies that enhance the understanding of causal relationships across diverse contexts.
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3.4 course rating
14 ratings
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