Statistical Horizons Seminar: Causal Inference with Directed Graphs

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A two-day seminar taught by Dr Felix Elwert

This seminar won the 2013 Causality in Statistics Education Award, given by the American Statistical Association.

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.

Materials

Participants receive a bound manual containing detailed lecture notes (with equations and graphics) and many other useful features. This book frees participants from the distracting task of note taking.
Registration and lodging

The fee of $895.00 includes all seminar materials. The early registration fee of $795 is March 4.

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 $142 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 STA403. For guaranteed rate and availability, you must reserve your room no later than March 3, 2014.

SEMINAR OUTLINE

1. Counterfactual causality
2. Directed Acyclic Graphs (DAGs)
a. Elements
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
a. Identification
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 instrumental variables analysis
a. “Controlled” and “natural” effects
b. Identification requirements
8. DAGs for mediation analysis
a. “Controlled” and “natural” effects
b. Identification requirements

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