Causal Inference Challenges that Go Beyond Statistics

This talk will delve into two major causal inference obstacles: (1) identifying which variables to account for and (2) assessing the impact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe’s Quartet, this is a set of four datasets with identical statistical properties, yet different true causal effects due to differing data generating mechanisms. These simple datasets provide a straightforward example for biostatisticians to point to when explaining these concepts to collaborators and students. Here, statistics can’t solve your causal inference problem because statistics alone cannot be used to establish which variables to adjust for when estimating causal effects. Statistics can help us explore the impact of unmeasured variables. The second half of the talk will focus on how statistical techniques can be leveraged to address unmeasured confounding. We will examine sensitivity analyses under several scenarios with varying levels of information about potential unmeasured confounders. These techniques will be applied using the tipr R package, which provides tools for conducting sensitivity analyses in a flexible and accessible manner.

April 10, 2023

12:15 PM – 1:15 PM

Johns Hopkins Bloomberg School of Public Health Department of Biostatistics Seminar Spring 2023