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Casual Inference

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Rating
★★★★★
4.6
from
111 reviews
This podcast has
68 episodes
Language
English
Date created
2019/11/01
Latest episode
2025/06/26
Average duration
52 min.
Release period
32 days

Description

Keep it casual with the Casual Inference podcast. Your hosts Lucy D'Agostino McGowan and Ellie Murray talk all things epidemiology, statistics, data science, causal inference, and public health. Sponsored by the American Journal of Epidemiology.

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Optimizing Data Workflows with Emily Riederer
2025/06/26
Emily Riederer is a Data Science Senior Manager at Credit Risk Modeling Capital One. Her website can be found here: https://www.emilyriederer.com/   Follow along on Bluesky: Emily: ‪@emilyriederer.bsky.social‬ Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social   🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.
Combining Data & Making Effects Generalizable with Carly Brantner
2025/06/17
Carly Brantner is an assistant professor of Biostatistics & Bioinformatics at Duke University and Duke Clinical Research Institute. Resources from this episode: multicate: R package for estimating conditional average treatment effects across one or more studies using machine learning methods PCORnet® Front Door: Access point for potential investigators, patient groups, and other stakeholders to connect with PCORnet and get support for potential research studies Patient-Centered Outcomes Data Repository (PDOCR): De-identified data from 24 (and counting) PCORI-funded studies Follow along on Bluesky: Carly: @carlybrantner.bsky.social Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social   🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.
The Art of Clarity with Andrew Heiss
2025/05/29
Andrew Heiss is an assistant professor in the Department of Public Management and Policy at the Andrew Young School of Policy Studies at Georgia State University. Vincent's "What is your estimand" section in his {marginaleffects} book: https://marginaleffects.com/chapters/challenge.html#sec-goals_estimand Article on defining estimands: https://doi.org/10.1177/00031224211004187 Andrew's marginal effects post: https://www.andrewheiss.com/blog/2022/05/20/marginalia/ Andrew's post on "fixed effects" and mariginal effects across different disciplines: https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/ Follow along on Bluesky: Andrew: @andrew.heiss.phd Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social   🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.
Study Critique: What Went Wrong and How We'd Do It Differently
2025/05/08
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: https://publichealthpolicyjournal.com/vaccination-and-neurodevelopmental-disorders-a-study-of-nine-year-old-children-enrolled-in-medicaid/ RFK Jr is promoting a new study claiming "vaccines cause autism" but it doesn't add up. Literally [Ellie's substack]: https://epiellie.substack.com/p/rfk-jr-is-promoting-a-new-study-claiming   Follow along on Bluesky: Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social   🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.
From Model to Meaning with Vincent Arel-Bundock
2025/04/24
Vincent Arel-Bundock is a professor at the Université de Montréal, where he studies comparative and international political economy. Vincent's website: https://arelbundock.com/ Vincent's book "Model to Meaning: How to Interpret Statistical Models With marginaleffects for R and Python": https://marginaleffects.com/     Follow along on Bluesky: Vincent: @vincentab.bsky.social Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social     🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.
Propensity Scores, R Packages, and Practical Advice with Noah Greifer
2025/04/10
Noah Greifer is a statistical consultant and programmer at Harvard University. Episode notes: WeightIt package: https://ngreifer.github.io/WeightIt/ MatchIt package: https://kosukeimai.github.io/MatchIt/ Noah's awesome Stack Exchange post: https://stats.stackexchange.com/a/544958 Follow along on Bluesky: Noah: @noahgreifer.bsky.social Ellie: @EpiEllie.bsky.social Lucy: @LucyStats.bsky.social 🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.      
Causal Assumptions and Large Language Models
2025/03/27
Lucy 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: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.
Data Integration for Impact with Len Testa | Season 6 Episode 1
2025/02/28
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 [Blog Post] Wall Street Journal Artcile, "Even Disney Is Worried About the High Cost of a Disney Vacation" [Article] Follow along on Bluesky: Len: @lentesta.bsky.social Ellie: @EpiEllie.bsky.social Lucy: @LucyStats.bsky.social 🎶 Our intro/outro music is courtesy of Joseph McDade
Starting the Conversation on Models with Alyssa Bilinski
2024/07/10
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: https://www.pnas.org/doi/full/10.1073/pnas.2302528120 Shuo Feng's pre-print: https://www.medrxiv.org/content/10.1101/2024.04.08.24305335v1 Our uncertainty paper: https://pubmed.ncbi.nlm.nih.gov/33475686/ Follow along on Twitter: Alyssa: @ambilinski The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Cameron Bopp
Flexible methods with Edward Kennedy
2024/06/26
Edward Kennedy Associate Professor, Department of Statistics & Data Science, Carnegie Mellon. ehkennedy.com Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California: https://academic.oup.com/aje/article/193/4/673/7425624 Doubly Robust Capture-Recapture Methods for Estimating Population Size: https://www.tandfonline.com/doi/full/10.1080/01621459.2023.2187814 Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Cameron Bopp
What Sports and Feminism can tell us about Causal Inference with Sheree Bekker & Stephen Mumford
2024/06/12
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, Department for Health, Centre for Qualitative Research Centre for Health and Injury and Illness Prevention in Sport Stephen Mumford, Professor of Metaphysics, Durham University  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, 2011 with Rani Lill Anjum), Metaphysics: a Very Short Introduction (Oxford, 2012) and Causation: a Very Short Introduction (Oxford, 2013 with Rani Lill Anjum). I was editor of George Molnar's posthumous Powers: a Study in Metaphysics (Oxford, 2003) and Metaphysics and Science (Oxford, 2013 with Matthew Tugby). Feminist Sport Lab: https://www.feministsportlab.com Causation: A Very Short Introduction by Stephen Mumford & Rani Lill Anjum: https://academic.oup.com/book/616 Faye Norby, Iditarod champion & epidemiologist: https://www.kfyrtv.com/2024/03/28/faye-norby-finishes-iditarod-trail-womens-foot-champion/?outputType=amp  Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Cameron Bopp
Observational Causal Analyses with Erick Scott
2024/05/29
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: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Cameron Bopp
Friends Let Friends Do Mediation Analysis with Nima Hejazi | Season 5 Episode 7
2024/05/16
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: https://www.jstor.org/stable/44955325 Nima is on Twitter/X as @nshejazi (https://twitter.com/nshejazi) and my academic webpage is https://nimahejazi.org Recent translational review paper (intended for the infectious disease science community) I was involved in describing some causal/statistical frameworks for evaluating immune markers as mediators / surrogate endpoints: https://pubmed.ncbi.nlm.nih.gov/38458870/ The tlverse software ecosystem is on GitHub at https://github.com/tlverse and the tlverse handbook is freely available at https://tlverse.org/tlverse-handbook/ Dr. Hejazi annually co-teaches a causal mediation analysis workshop at SER, and notes from the latest offering are freely available at https://codex.nimahejazi.org/ser2023_mediation_workshop/ Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Cameron Bopp
Fun and Game(s) Theory with Aaditya Ramdas
2024/05/01
Aaditya Ramdas is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. His research interests include game-theoretic statistics and sequential anytime-valid inference, multiple testing and post-selection inference, and uncertainty quantification for machine learning (conformal prediction, calibration). His applied areas of interest include neuroscience, genetics and auditing (real-estate, finance, elections). Aaditya received the IMS Peter Gavin Hall Early Career Prize, the COPSS Emerging Leader Award, the Bernoulli New Researcher Award, the NSF CAREER Award, the Sloan fellowship in Mathematics, and faculty research awards from Adobe and Google. He also spends 20% of his time at Amazon working on causality and sequential experimentation. Aaditya's website: https://www.stat.cmu.edu/~aramdas/ Game theoretic statistics resources Aaditya's course, Game-theoretic probability, statistics, and learning: https://www.stat.cmu.edu/~aramdas/gtpsl/index.html Papers of interest: Time-uniform central limit theory and asymptotic confidence sequences: https://arxiv.org/abs/2103.06476 Game-theoretic statistics and safe anytime-valid inference: https://arxiv.org/abs/2210.01948 Discussion papers: Safe Testing: https://arxiv.org/abs/1906.07801 Testing by Betting: https://academic.oup.com/jrsssa/article/184/2/407/7056412 Estimating means of bounded random variables by betting: https://academic.oup.com/jrsssb/article/86/1/1/7043257  Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Cameron Bopp
Cookies, Causal Inference, and Careers with Ingrid Giesinger #Epicookiechallenge
2024/04/17
Ingrid is a doctoral student in Epidemiology at the Dalla Lana School of Public Health at the University of Toronto.  Winning cookie recipe Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Cameron Bopp

Podcast reviews

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4.6 out of 5
111 reviews
★★★★★
kkflo00177 2025/04/28
Casual voices = casual infer!
Two things. First and most importantly, this is among the very best, most fun, most useful podcasts I’ve found. Thank you both for the time and work t...
★★★★☆
Complemental 2025/08/19
Best causal inference podcast
My only wish is for more input from sciences outside biology and from the computer science side of causal inference research. A lot is being done well...
★★★★☆
Megjhart 2025/07/02
Great content
I absolutely love the content of this podcast. Everything about it. I just listened to the optimizing data workflows episode and the tone and use of f...
★★★★★
Bill Jesdale 2024/06/12
Thoughtful yet Approachable Dive
Casual Inference is a thoughtful yet approachable dive into contemporary issues in epi, I recommend it to my students, and the faculty here love to ta...
★★★☆☆
MelFierros 2024/08/12
Vocal Fry
Love the concept, the hosts are very knowledgeable. However, it’s so difficult to focus on what the topics are when the voices quite literally make my...
★★★★★
Grschunchibdseyv 2024/06/05
Modern science of making sense from greasy data
Drs. Murray and D’Agostino-Gowan provide the content that reflects the state of the art in the relatively recent interdisciplinary area of scientific ...
★★★★★
breggurns 2023/08/31
Excellent podcast!
I would highly recommend this podcast to anyone interested in Epi/Biostats! Excellent job, this is quickly becoming one of my favorite listens while d...
★★★☆☆
NickiMina 2024/02/23
Great conceptually but vocal fry undermines experience as a listener
Highly interested in the episodes and guests but unfortunately find it grating to listen to. Many people do not have natural speaking voices that are ...
★★★★★
Pete Amer 2023/07/23
Great podcast
This is a really fun and informative podcast on causal inference and data science. The hosts both are great at communicating topics in research design...
★★★★★
Dylan_Pete 2023/04/11
Great Podcast!
I love this podcast. Lucy and Ellie help me find joy in learning causal inference and enjoy their sense of humor!
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