Course Summary
This course teaches the principles of causal inference, with a focus on randomized experiments and observational studies. Students will learn how to design experiments, how to analyze data from experiments and observational studies, and how to use causal inference to make predictions.Key Learning Points
- Learn the principles of causal inference
- Focus on randomized experiments and observational studies
- Design experiments, analyze data and use causal inference to make predictions
Related Topics for further study
Learning Outcomes
- Understand the principles of causal inference
- Learn how to design experiments and analyze data
- Use causal inference to make predictions
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with programming in R or Python
Course Difficulty Level
IntermediateCourse Format
- Online Course
- Self-paced
- Video Lectures
- Assignments
- Quizzes
Similar Courses
- Data Analysis and Statistical Inference
- Causal Diagrams: Draw Your Assumptions Before Your Conclusions
- Applied Data Science with Python
Related Education Paths
Notable People in This Field
- Judea Pearl
- Donald Rubin
Related Books
Description
This course offers a rigorous mathematical survey of advanced topics in causal inference at the Master’s level.
Outline
- Module 7: Introduction to Mediation
- Introduction to Causal Inference 2
- Lesson 1: Mediation and Conditioning on Intermediate Outcomes
- Lesson 2: Reframing the Problem of Mediation
- Lesson 3: Identification of Controlled, Average Direct and Indirect Effects
- Welcome to Module 7
- Intro Survey
- Module 8: More on Mediation
- Lesson 1: Estimation of Mediated Effects
- Lesson 2: Sensitivity Analyses for Mediation
- Lesson 3: Instrumental Variables with a Continuous Treatment
- Welcome to Module 8
- Module 9: Instrumental Variables, Principal Stratification, and Regression Discontinuity
- Lesson 1: Instrumental Variables and the Complier Average Causal Effect
- Lesson 2: Principal Stratification
- Lesson 3: Regression Discontinuity
- Welcome to Module 9
- Module 10: Longitudinal Causal Inference
- Lesson 1: The g-formula
- Lesson 2: Marginal Structural Models
- Lesson 3: Structural Nested Mean Models and g-estimation
- Welcome to Module 10
- Module 11: Interference and Fixed Effects
- Lesson 1: Introduction to Interference
- Lesson 2: Interference Continued
- Lesson 3: Fixed Effects Regressions in Econometrics
- Welcome to Module 11
- Exit Survey
- Module 11: Assessment
Summary of User Reviews
Causal Inference 2 is a highly recommended course that teaches how to identify and measure causal effects. It has received many positive reviews from users who found the course to be informative, engaging, and well-structured.Key Aspect Users Liked About This Course
Many users thought the real-life examples used in the course were very helpful and made the concepts easier to understand.Pros from User Reviews
- The course is taught by experienced professors who are experts in the field of causal inference
- The course is well-structured and easy to follow
- The real-life examples used in the course are very helpful and make the concepts easier to understand
- The course provides a good balance between theory and practice
- The assignments and quizzes are challenging but also help reinforce the concepts learned
Cons from User Reviews
- Some users found the course to be too technical and difficult to follow
- The course requires a strong background in statistics and mathematics
- Some users felt that the course could have covered more advanced topics
- The course does not provide much opportunity for interaction with other students
- The course does not offer a certificate of completion for free
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