Beyond Associations: Mediation Analysis for Causal Inference

May 8, 2018 · St. Louis, United States of America

Speaker: Lorinette S. Wirth, MPH, PhD student

Maximizing data insights warrants delving deeper into understanding the relationships between variables past mere associations. Causal Inference techniques enhance the utility of secondary data, allowing data analysts to make more robust conclusions about indirect and direct relationships. Data driven operations, from policy and medical decision making to optimization of customer experience and sales, can benefit greatly from this methodology. Mediation Analysis is a type of causal inference that is used to determine whether a variable, M, is in the causal pathway between an independent variable, X, and dependent variable, Y i.e. X -> M -> Y.

The first 20-minutes of this session will be spent introducing the field of Causal Inference and the theory of Mediation Analysis. The following 30-minutes will be dedicated to utilizing the R software to conduct Mediation Analyses. The remaining time will be dedicated to questions and answers. Participants are expected to have the R software already downloaded at the time of the workshop. All experience levels are welcomed.

Lorinette S. Wirth is a Biostatistician, Epidemiologist, and Data Scientist. She is also a PhD student in Public Health Studies- Health Outcomes Research at Saint Louis University.

Join us for networking at 6:00 pm over light refreshments. The presentation will begin at 6:30. Don't forget your laptop!

Event organizers
  • St. Louis Machine Learning & Data Science

    We seek to build and connect the Machine Learning community in St. Louis. Our meetings revolve around Machine Learning, NLP, AI, statistics, big data, and any other topics that fit under the realm of Data Science. We'll try to mix discussions of the most recent trends in academia with practical applications and hands-on coding. All skill and experience are welcome.

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