Many important questions about cause and effect are impractical to answer with a randomized experiment. What should we do instead? In this episode we talk about doing causal inference with observational data. Has psychology's historical obsession with internal validity led it, ironically, to think about causal inference in an unsophisticated way? Can formal analytic tools like directed acyclic graphs (DAGs) tell us how to do better studies? Or is their main lesson don't bother trying? How do norms and incentives in publishing help or hurt in doing better causal inference? Plus: We answer a letter about applying to psychology grad school when your background is in data science.
The Black Goat is hosted by Sanjay Srivastava, Alexa Tullett, and Simine Vazire. Find us on the web at www.theblackgoatpodcast.com, on Twitter at @blackgoatpod, on Facebook at facebook.com/blackgoatpod/, and on instagram at @blackgoatpod. You can email us at email@example.com. You can subscribe to us on iTunes or Stitcher.
Our theme music is Peak Beak by Doctor Turtle, available on freemusicarchive.org under a Creative Commons noncommercial attribution license. Our logo was created by Jude Weaver.
This is episode 76. It was recorded on March 16, 2020.
You Took the Words Right Out of My Mouth
They Give You This, But You Pay For That
An Award-Winning Episode
Does Not Compute
The Impending Fall of Academia
Joe Public, Will You Marry Me?
Auxiliary Turtles All the Way Down
The Expertise of Death
Going Off the Record
The Year 2019 in Review
Letting Loose Your Inner Reviewer Two
The Last Straw
Talk the Talk
Everybody Act Normal
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