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Causal Inference with Directed Graphs

Event Details

Causal Inference with Directed Graphs

Time: April 8, 2016 at 9am to April 9, 2016 at 4pm
Location: Temple University Center City
Street: 1515 Market Street
City/Town: Philadelphia, PA 19102
Website or Map: http://statisticalhorizons.co…
Phone: 610-642-1941
Event Type: training, seminar
Organized By: Statistical Horizons LLC
Latest Activity: Feb 2, 2016

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Event Description

Taught by Dr. Felix Elwert, Ph.D.

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. 

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