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Causal Inference

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)

18,266 already enrolled

Instructors:

English

English (Original), Deutsch (Auto), हिन्दी (ऑटो), 18 more

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Causal Inference

This course includes

12 Hours

Of Self-paced video lessons

Advanced Level

Completion Certificate

awarded on course completion

2,435

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

causal inference
statistical methods
propensity score
randomization
regression analysis
machine learning
treatment effects
observational studies

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

Michael E. Sobel
Michael E. Sobel

4.2 rating

25 Reviews

19,972 Students

2 Courses

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.

Causal Inference

This course includes

12 Hours

Of Self-paced video lessons

Advanced Level

Completion Certificate

awarded on course completion

2,435

Testimonials

Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.

3.4 course rating

97 ratings

Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.