Causal Inference with Directed Graphs
A 2-Day Seminar Taught by Felix Elwert, Ph.D.
This seminar won the 2013 Causality in Statistics Education Award, given by the American Statistical Association.
To see a sample of the course materials, click here.
This seminar offers an applied introduction to directed acyclic graphs (DAGs) for causal inference. DAGs are a powerful new tool for understanding and resolving causal problems in empirical research. DAGs are useful for social and biomedical researchers, business and policy analysts who want to draw causal inferences from non-experimental data. The chief advantage of DAGs is that they are “algebra-free,” relying instead on intuitive yet rigorous graphical rules.
The two primary uses of DAGs are (1) determining the identifiability of causal effects from observed data, and (2) deriving the testable implications of a causal model. DAGs are also helpful for understanding the causal assumptions behind widely used estimation strategies, such as regression, matching, and instrumental variables analysis.
This seminar will focus on building transferable intuition and skills for applied causal inference. We start by introducing the essential elements for causal reasoning with DAGs and then use DAGs to discuss a range of important challenges in observational data analysis. Topics include: conditions for the identification of causal effects; d-separation; the 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 and instrumental variables analysis; and recent work on causal mediation analysis.
Please note that this seminar will empower participants to recognize and understand problems and to spot fresh opportunities for causal inference. This seminar does not introduce new estimators and has no software component.
Who should attend?
If you want to understand under what circumstances you can draw causal inferences from non-experimental data, this course is for you. Participants should have a good working knowledge of multiple regression and basic concepts of probability. Some prior exposure to causal inference (counterfactuals, propensity scores, instrumental variables analysis) will be helpful but is not essential.
Since this seminar aims to strengthen your ability to think through causal problems we will work through numerous pencil-and-paper exercises. You will learn all necessary technical tools in this seminar. You do not need to know matrix algebra or calculus. There is no software component.
LOCAtions, Format, And Materials
The class will meet from 9 am to 4 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and lodging
The fee of $995.00 includes all seminar materials.
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $159 per night. This location is about a 5 minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code SH0419 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, March 19, 2018.
If you make reservations after the cut-off date, ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request.
1. Counterfactual causality
2. Directed Acyclic Graphs (DAGs)
b. Graphical display of the data generating model
c. The importance of causal assumptions
3. Associational implications of a causal model
a. Association vs. causation in DAGs
b. Three sources of association and independence (d-separation)
c. The difference between confounding and selection bias
d. Deriving testable implications
4. Graphical Identification Criteria
b. Control for confounding via adjustment
c. Adjustment and ignorability
d. Back-door identification
e. Front-door identification
f. Some helpful sufficient rules
5. Selection Bias
a. Post-outcome selection examples
b. Intermediate-variable selection examples
c. Pre-treatment selection bias examples
d. Why selection and confounding are distinct causal concepts
6. Graphical insights for common methods
a. Identification in matching and regression
b. Hidden causal assumptions in regression analysis
7. DAGs for mediation analysis
a. “Controlled” and “natural” effects
b. Identification requirements
“I have spent years reading literatures on this topic and Dr. Elwert virtually provided a complete illumination of Causal Inference with Directed Graphs in just two days. Save yourself time, struggles, and confusion by taking this wonderful class!”
Joseph Staats, University of Minnesota Duluth
“Felix is an excellent instructor of this course and going through his training has helped me to understand the important basics of DAGs and graphical analysis as well as their importance in causal inference. I would highly recommend this course to anyone involved in causal inference in whatever area of specialty because, believe you me, this instructor is good!”
Edmore Chamappiwa, University of Manchester, UK
“Every scientist engaged in biomedical research should take this course. Understanding causal inference is critical for conducting research that can impact the quality of life of patients, advance the science of medicine, and promote the health and welfare of all.”
Debbie Barrington, Georgetown University
“As an epidemiologist who was not taught DAGs in their training, and who has attempted to read articles explaining the rules of DAGS, this class was great. Left me feeling more empowered in my discipline.”
“My understanding of research design changed after this course. This is one of the most powerful courses in methods I have ever encountered.”
Anibal Perez-Linan, University of Pittsburgh
“This course helps reinforce epidemiologic methods through DAGs. It was extremely valuable, fun and worth participation. It gave me a great mental tune-up.”
Ximena Vergara, Electric Power Research Institute/UCLA
“Causal Inference with Directed Graphs provides a better alternative in making causal inferences from regression analysis. The tutor is good and experienced at the subject and you can be sure of a better understanding at the end of the workshop.”
Godfred Boateng, Cornell University
“The course opened my eyes to some causal problems linked to ordinary least squares regression analysis. It was also impressive that Professor Elwert knew our names that fast.”
Henk Von Eije, University of Groningen
“Felix is an amazing teacher. Explains the basics (and advanced) stuff with such ease. I have been reading a lot to understand causality for a long time, but this course made it look simple and cool. I really want to take my learning forward by applying DAG to test my results from previous analysis.”
Vivek Kumar Sundriyal, Lund University