CTSI BERD Workshop Webinar

Causal Inferences for Experimental and Quasi-Experimental Designs

Causal inference is central to the social, behavioral, and biomedical sciences. Although our research questions tend to be causal in nature, whether causal claims can be made depends on whether a list of assumptions are met regarding study design and statistical analyses. In this workshop, John Felt will present approaches for designing experimental and quasi-experimental studies that facilitate causal inference from the perspective of three complimentary schools of thought: Potential Outcomes, Plausible Threats to Validity, and Directed Acyclic Graphs (DAGs). Hands-on experience with analytic approaches to facilitate causal inference will also be provided in R, a free and open-source statistical computing and graphics software. Users are encouraged to download and install R and R Studio prior to the workshop. A tutorial for downloading R and R Studio can be found here. Participants will leave this workshop with an introductory understanding of casual inference that they can start implementing in studies with new data collection as well as in secondary data analyses.

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