A Crash Course in Causality: Inferring Causal Effects from Observational Data
- 4.7
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Approx. 18 hours to complete
Course Summary
Learn the basics of causality and how to apply it in different fields such as medicine, social science, and engineering with this crash course.Key Learning Points
- Understand the fundamentals of causality and its importance in research and decision making.
- Learn different methods for causal inference such as randomized control trials and natural experiments.
- Apply causal inference to real-world problems in medicine, social science, and engineering.
Related Topics for further study
Learning Outcomes
- Understand the basics of causality and its role in research and decision making.
- Identify and apply different methods of causal inference.
- Apply causal inference to real-world problems in medicine, social science, and engineering.
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics and probability.
- Familiarity with research methodologies.
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Video lectures
- Quizzes
Similar Courses
- Causality: Models, Reasoning, and Inference
- Introduction to Epidemiology
- Data Analysis and Statistical Inference
Related Education Paths
Notable People in This Field
- Statistician and Founder of FiveThirtyEight
- Psychologist and Nobel Prize Winner
Related Books
Description
We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more!
Outline
- Welcome and Introduction to Causal Effects
- Welcome to "A Crash Course in Causality"
- Confusion over causality
- Potential outcomes and counterfactuals
- Hypothetical interventions
- Causal effects
- Causal assumptions
- Stratification
- Incident user and active comparator designs
- Practice Quiz
- Practice Quiz
- Causal effects
- Confounding and Directed Acyclic Graphs (DAGs)
- Confounding
- Causal graphs
- Relationship between DAGs and probability distributions
- Paths and associations
- Conditional independence (d-separation)
- Confounding revisited
- Backdoor path criterion
- Disjunctive cause criterion
- Practice Quiz
- Identify from DAGs sufficient sets of confounders
- Matching and Propensity Scores
- Observational studies
- Overview of matching
- Matching directly on confounders
- Greedy (nearest-neighbor) matching
- Optimal matching
- Assessing balance
- Analyzing data after matching
- Sensitivity analysis
- Data example in R
- Propensity scores
- Propensity score matching
- Propensity score matching in R
- Practice Quiz
- Practice Quiz
- Matching
- Propensity score matching
- Data analysis project - analyze data in R using propensity score matching
- Inverse Probability of Treatment Weighting (IPTW)
- Intuition for Inverse Probability of Treatment Weighting (IPTW)
- More intuition for IPTW estimation
- Marginal structural models
- IPTW estimation
- Assessing balance
- Distribution of weights
- Remedies for large weights
- Doubly robust estimators
- Data example in R
- Practice Quiz
- IPTW
- Data analysis project - carry out an IPTW causal analysis
- Instrumental Variables Methods
- Introduction to instrumental variables
- Randomized trials with noncompliance
- Compliance classes
- Assumptions
- Causal effect identification and estimation
- IVs in observational studies
- Two stage least squares
- Weak instruments
- IV analysis in R
- Practice Quiz
- Practice Quiz
- Instrumental variables / Causal effects in randomized trials with non-compliance
Summary of User Reviews
Discover the fundamentals of causality and its importance in data analysis with Crash Course in Causality. This course has received positive reviews from users who found it informative and engaging. Many users appreciated the clear explanations and practical examples given throughout the course.Key Aspect Users Liked About This Course
Clear explanations and practical examplesPros from User Reviews
- Course content is well-organized and easy to follow
- Instructors are knowledgeable and responsive to questions
- Course provides a solid foundation for further study in causality
- Real-world examples and case studies are helpful in understanding concepts
Cons from User Reviews
- Some users found the course too basic and not challenging enough
- The pace of the course may be too slow for some learners
- The course may not be suitable for those with advanced knowledge of causality
- Some users found the course repetitive in certain areas
Jason A. Roy, Ph.D.
4.7 Raiting
A Crash Course in Causality: Inferring Causal Effects from Observational Data
- 4.7
-
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![]()
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We have all heard the phrase “correlation does not equal causation. ” What, then, does equal causation? This course aims to answer that question and more!...
