Start course
Image: medium.com
About Course
Welcome to introduction to causal inference course. In this course, participants will learn about the methods in causal inference with observational data, including: potential outcomes; DAGs and causal graphs; target trial emulation; matching, handling confounding and bias, causal structure learning; quasi-experimental methods such as instrumental variable methods, regression discontinuity design, difference in difference; and a variety of g methods for handling time varying confounders.
Course Content(s)
- Introduction to causal inference
- Confounding, Bias and Paradox
- Causal Models: Potential Outcome framework
- Graphical Models/Directed Acyclic Graphs
- Instrumental variable Methods
- Regression Discontinuity Design
- Difference in Difference (DID)
- Matching and Stratification
- Introduction to statistical learning
- Application of g-methods in Mental Health Data
- Targeted Emulation Trials
Course Instructor(s)
Mulusew J. Gerbaba
Course Objective(s)
The aim of the course is to introduce participants to the modern causal modeling methodology. By the end of the course, participants will be able to:
• Define causal research questions
• Learn Data generative process that represent their substantive knowledge in the form of a causal graph
• Specify a target trial and use this to guide analytic choices
• Investigate whether causal effects are identified in different settings
• Choose which variables they need to adjust for to estimate causal effects
• Estimate causal effects using simple and more advanced confounder adjustment methods in different settings
• Apply causal learning techniques to their data
• Have a working knowledge of different quasi-experimental causal inference techniques
• Define causal research questions
• Learn Data generative process that represent their substantive knowledge in the form of a causal graph
• Specify a target trial and use this to guide analytic choices
• Investigate whether causal effects are identified in different settings
• Choose which variables they need to adjust for to estimate causal effects
• Estimate causal effects using simple and more advanced confounder adjustment methods in different settings
• Apply causal learning techniques to their data
• Have a working knowledge of different quasi-experimental causal inference techniques
Course Content(s)
- Introduction to causal inference
- Confounding, Bias and Paradox
- Causal Models: Potential Outcome framework
- Graphical Models/Directed Acyclic Graphs
- Instrumental variable Methods
- Regression Discontinuity Design
- Difference in Difference (DID)
- Matching and Stratification
- Introduction to statistical learning
- Application of g-methods in Mental Health Data
- Targeted Emulation Trials
Course Instructor(s)
Mulusew J. Gerbaba
Course Objective(s)
The aim of the course is to introduce participants to the modern causal modeling methodology. By the end of the course, participants will be able to:
• Define causal research questions
• Learn Data generative process that represent their substantive knowledge in the form of a causal graph
• Specify a target trial and use this to guide analytic choices
• Investigate whether causal effects are identified in different settings
• Choose which variables they need to adjust for to estimate causal effects
• Estimate causal effects using simple and more advanced confounder adjustment methods in different settings
• Apply causal learning techniques to their data
• Have a working knowledge of different quasi-experimental causal inference techniques
• Define causal research questions
• Learn Data generative process that represent their substantive knowledge in the form of a causal graph
• Specify a target trial and use this to guide analytic choices
• Investigate whether causal effects are identified in different settings
• Choose which variables they need to adjust for to estimate causal effects
• Estimate causal effects using simple and more advanced confounder adjustment methods in different settings
• Apply causal learning techniques to their data
• Have a working knowledge of different quasi-experimental causal inference techniques