Master's-level course on statistical methods for causal inference, covering experimental design, matching, propensity scores, and machine learning approaches.
Master's-level course on statistical methods for causal inference, covering experimental design, matching, propensity scores, and machine learning approaches.
This rigorous mathematical course explores advanced statistical methods for causal inference at the Master's level. Led by Professor Michael E. Sobel, it covers revolutionary developments in causal inference from the past 35-40 years. Students learn to distinguish causal from non-causal relationships and master various estimation methods including matching, propensity scoring, and machine learning approaches. The course emphasizes both theoretical understanding and practical application of causal inference in science, medicine, policy, and business contexts.
3.4
(97 ratings)
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Instructors:
English
English (Original), Deutsch (Auto), हिन्दी (ऑटो), 18 more
What you'll learn
Master fundamental concepts of causal inference and potential outcomes
Understand randomization inference and experimental design
Apply regression-based approaches to causal estimation
Implement propensity score matching and weighting methods
Use machine learning techniques for treatment effect estimation
Evaluate and test causal assumptions
Skills you'll gain
This course includes:
193 Minutes PreRecorded video
5 assignments
Access on Mobile, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course provides a rigorous mathematical introduction to causal inference at the Master's level. The curriculum covers essential topics in modern causal analysis, including experimental design, treatment effects estimation, and advanced statistical methods. Students learn to apply various techniques such as matching, propensity score analysis, and machine learning approaches to estimate causal relationships. The course emphasizes both theoretical foundations and practical applications in research, policy, and business settings.
MODULE 1: Key Ideas
Module 1 · 1 Hours to complete
Module 2: Randomization Inference
Module 2 · 2 Hours to complete
MODULE 3: Regression
Module 3 · 2 Hours to complete
Module 4: Propensity Score
Module 4 · 2 Hours to complete
Module 5: Matching
Module 5 · 2 Hours to complete
Module 6: Special Topics
Module 6 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
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
97 ratings
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