Causal Assumptions and Large Language Models | Season 6 Episode 2
Release Date: 03/27/2025
Study Critique: What Went Wrong and How We'd Do It Differently | Season 6 Episode 5
Casual Inference
In this episode Lucy and Ellie dig into a recently publicized paper, "Vaccination and Neurodevelopmental Disorders: A Study of Nine-Year-Old Children Enrolled in Medicaid", which has gained attention after being promoted by RFK Jr. as evidence that vaccines cause autism. Ellie breaks down her Substack critique of the study. Together, she and Lucy discuss the methodological flaws and what a better version of this study might look like. Vaccination and Neurodevelopmental Disorders: A Study of Nine-Year-Old Children Enrolled in Medicaid: RFK Jr is promoting a new...
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From Model to Meaning with Vincent Arel-Bundock | Season 6 Episode 4
Casual Inference
Vincent Arel-Bundock is a professor at the Université de Montréal, where he studies comparative and international political economy. Vincent's website: Vincent's book "Model to Meaning: How to Interpret Statistical Models With marginaleffects for R and Python": Follow along on Bluesky: Vincent: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of . Edited by .
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Propensity Scores, R Packages, and Practical Advice with Noah Greifer | Season 6 Episode 3
Casual Inference
Noah Greifer is a statistical consultant and programmer at Harvard University. Episode notes: WeightIt package: MatchIt package: Noah's awesome Stack Exchange post: Follow along on Bluesky: Noah: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of . Edited by .
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Causal Assumptions and Large Language Models | Season 6 Episode 2
Casual Inference
Lucy and Ellie chat about large language models, chat interfaces, and causal inference. Do LLMs Act as Repositories of Causal Knowledge?: Follow along on Twitter: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of . Edited by .
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Data Integration for Impact with Len Testa | Season 6 Episode 1
Casual Inference
Lucy chats with Len Testa about a recent analysis he did which combined over 150 publicly available data sources to answer a question about the affordability of Disney World. Len's Deep Dive Post on the Touring Plans Blog [] Wall Street Journal Artcile, "Even Disney Is Worried About the High Cost of a Disney Vacation" [] Follow along on Bluesky: Len: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of
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Starting the Conversation on Models with Alyssa Bilinski | Season 5 Episode 11
Casual Inference
Alyssa Bilinski, Peterson Family Assistant Professor of Health Policy, and Assistant Professor of Biostatistics, at Brown University School of Public Health. Her research focuses on developing novel methods for policy evaluation and applying these to identify interventions that most efficiently improve population health and well-being. Episode notes: PNAS paper: Shuo Feng’s pre-print: Our uncertainty paper: Follow along on Twitter: Alyssa: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of Edited by
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Flexible methods with Edward Kennedy | Season 5 Episode 10
Casual Inference
Edward Kennedy Associate Professor, Department of Statistics & Data Science, Carnegie Mellon. Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California: Doubly Robust Capture-Recapture Methods for Estimating Population Size: Follow along on Twitter: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of Edited by
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What Sports and Feminism can tell us about Causal Inference with Sheree Bekker & Stephen Mumford | Season 5 Episode 9
Casual Inference
Sheree Bekker & Stephen Mumford are Co-directors of the Feminist Sport Lab and have a book coming soon: “Open Play: the case for feminist sport”, coming Spring 2025. Reaktion Books (UK), University of Chicago Press (US). Sheree Bekker: Associate Professor, University of Bath, , Stephen Mumford, Professor of Metaphysics, A Author of Dispositions (Oxford, 1998), Russell on Metaphysics (Routledge, 2003), Laws in Nature (Routledge, 2004), David Armstrong (Acumen, 2007), Watching Sport: Aesthetics, Ethics and Emotion (Routledge, 2011), Getting Causes from Powers (Oxford,...
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Observational Causal Analyses with Erick Scott | Season 5 Episode 8
Casual Inference
Erick Scott is founder of cStructure, a causal science startup. Erick has expertise in medicine, public health, and computational biology. [email protected] “A causal roadmap for generating high-quality real-world evidence” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603361/ Follow along on Twitter: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of Edited by
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Friends Let Friends Do Mediation Analysis with Nima Hejazi | Season 5 Episode 7
Casual Inference
Nima Hejazi is an assistant professor in biostatistics at Harvard University. His methodological work often draws upon tools and ideas from semi- and non-parametric inference, high-dimensional and large-scale inference, targeted or debiased machine learning (e.g., targeted minimum loss estimation, method of sieves), and computational statistics. Surprised by the Hot Hand Fallacy? A Truth in the Law of Small Numbers by Joshua B. Miller & Adam Sanjurjo: Nima is on Twitter/X as @nshejazi () and my academic webpage is Recent translational review paper (intended for the infectious...
info_outlineLucy and Ellie chat about large language models, chat interfaces, and causal inference.
- Do LLMs Act as Repositories of Causal Knowledge?: https://arxiv.org/html/2412.10635v1
Follow along on Twitter:
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The American Journal of Epidemiology: @AmJEpi
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Ellie: @EpiEllie
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Lucy: @LucyStats
🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.