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Directed Acyclic Graphs for Causal Inference
Taught by Felix Elwert, Ph.D.
Tuesday, August 10 – Friday, August 13, 2021
Directed acyclic graphs (DAGs) are an algebra-free tool for understanding and resolving causal issues in empirical research. This seminar offers an applied introduction to DAGs for causal inference.
You will learn the essential elements for causal reasoning with DAGs and then we will use DAGs to discuss a range of important challenges in observational data analysis.
This seminar will empower participants to recognize and understand problems and to spot fresh opportunities for causal inference in their own data.
Topics include:
- conditions for the identification of causal effects
- d-separation
- he difference between confounding, over-control, and selection bias
- identification by adjustment
- backdoor identification
- what variables to control for in observational research
- what variables not to control for in observational research
- structural assumptions in regression
- instrumental variables analysis.
Join via Zoom to participate in lectures, receive hands-on exercise assignments, and interact with the instructor. Although we recommend joining the seminar live, you also have the option to take the course asynchronously by viewing recorded videos of the lectures.
Daily Zoom Seminar Schedule:
11:00 AM - 2:00 PM ET (New York time): Live lecture via Zoom
4:00 PM - 5:00 PM ET: Live lab session via Zoom (Tuesday & Friday Only) Learn More View our complete seminar list here.
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