Sensitivity Analysis: Quantifying the Discourse About Causal Inference

Session Type: Professional Development Course

Abstract

Participants will learn how to quantify concerns about causal inferences due to unobserved variables or populations. Participants will learn how to calculate the correlations associated with an unobserved confounding variable or the amount of one’s sample that would have to be replaced to invalidate an inference. I will also present a general framework for characterizing the robustness of inferences from randomized experiments or observational studies. Calculations for bivariate and multivariate analysis will be presented in SPSS, SAS, and Stata, with an excel spreadsheet for other applications. Additional topics include a typology of thresholds for making inferences, null hypotheses of non-zero effects, evaluating thresholds relative to characteristics of observed variables or populations, and extensions to non-linear models. The format will be a mixture of presentation, individual exploration, and group work. Participants may include graduate students and professors, although all must be comfortable with basic regression and multiple regression. Participants should bring their own laptop, or be willing to work with another student who has a laptop. Participants may choose to bring to the course an example of an inference from a published study or their own work, as well as data analyses they are currently conducting.

Sub Unit

• Professional Development and Training Committee

Instructors

• Kenneth A. Frank, Michigan State University

• Yun-Jia Lo, University of Michigan - Ann Arbor

• Michael H. Seltzer, University of California - Los Angeles

• Min Sun, University of Washington - Seattle

• Yuqing Liu, Michigan State University

• Jihyun Kim, Michigan State University

• I-Chien Chen, Michigan State University