Directed Acyclic Graphs for Causal Inference
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 is a hands–on course with at least one hour each day devoted to carefully structured and supervised assignments.
This course is a hands-on course with approximately 2 hours of instructor-led software demonstrations and approximately 2 hours of guided exercises. You should bring a laptop with a recent version of Stata installed (release 13 or later).
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 5 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. The early registration fee of $895.00 is available until September 18.
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 $177 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 SH1017 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, September 17, 2019.
If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) 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 wholeheartedly recommend this course to all empirically minded doctoral students – as early as possible in the process, before statistics/econometrics courses shift their attention away from theory/causal mechanisms towards estimation/data.”
Thorsten Sellhorn, Ludwig-Maximilians-Universitaet
“This was an excellent course. The instructor is very knowledgeable about the topic and fluent in multiple research disciplines. Uses concrete examples from a variety of disciplines. Combines theory with application. Highly recommended.”
“This course really helps with identifying situations where causal effects can be extracted and not. Very useful for experimental researchers also.”
“The seminar offers immediately applicable, lucidly presented lessons on applying causal logic to research design and analysis.”
Michael Lorber, New York University