Assuming in public
Curious about directed acyclic graphs, or DAGs, and how they can help your research? Confused about collider bias and how to avoid it? Watch Jon use the “parrot” method (you know, where he parrots people smarter than him) to stumble and sometimes curse his way through an unscripted but mostly accurate1 talk on these topics. It’s about an hour long and free - so you get what you pay for!
This talk was delivered to Indiana University Bloomington’s Social Science Research Commons as part of their Workshop in Methods Series.
Presentation Summary:
“Scientists routinely make causal inferences – whether implicit or explicit – about correlations generated from statistical analyses of experimental and observational data. However, while theorized causes are usually directionally specific, correlations are inherently symmetric or directionally ambiguous. Moreover, multiple causal structures can produce equivalent correlational results, posing significant threats to the validity of statistical inferences.
Fortunately, advances from the “causal revolution” in science and statistics have provided us with powerful tools, such as potential outcomes and directed acyclic graphs (DAGs), to better understand causes and effects. This talk will focus on how DAGs can help us “assume in public” more effectively. By introducing DAGs early in the research workflow and adhering to simple rules for their use, we can formalize the causal assumptions underlying our theories and statistical models, thereby enhancing transparency and reducing avoidable biases in causal estimation.
The presentation will cover the four foundational structures in causal systems, as represented in DAGs: complete independence, pipes, forks, and colliders. Real-world and simulated examples – drawn from the speaker’s blog posts – will illustrate key concepts, such as d-separation, good and bad controls, and adjustment sets. Finally, the talk will introduce tools and resources to help researchers more confidently and effectively navigate the assumptions and challenges of causal inference.”
Slides available on IU ScholarWorks
Video available on IU Digital Media Collection
Footnotes
For example, in rushing through a discussion of deterministic variables on slide 48, I incorrectly referenced collider bias instead of confounding bias. That is, in the depicted DAG, two component variables created forking backdoor paths between the deterministic ratio variable and the outcome; see our post on mixed signals for a detailed explanation.↩︎
Reuse
Citation
@online{brauer2025,
author = {Brauer, Jon},
title = {Assuming in Public},
date = {2025-03-06},
url = {https://reluctantcriminologists.com/blog-posts/[14]/assume-in-public.html},
langid = {en}
}