Casual Inference

00:43:32

Regression to the Mean Girls | The Marshmallow Test...

Casual Inference ·

2026/10/05

Follow Regression to the Mean Girls on [⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠], [⁠⁠⁠⁠Spotify⁠⁠⁠⁠], or wherever you love to listen!Mattan Ben-Shachar⁠ joins ⁠⁠⁠⁠⁠⁠⁠⁠Sarah Lotspeich⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠Lucy D'Agostino McGowan⁠⁠⁠⁠⁠⁠⁠⁠⁠ with a data-driven side quest into whether different colored marshmallows taste different. *Be sure to listen to the end for a surprising addendum!Check out the full process here: ⁠⁠⁠⁠⁠⁠⁠⁠regressiontothemeangirls.com⁠⁠⁠⁠⁠⁠⁠⁠Follow along on

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00:42:46

Regression to the Mean Girls | Election Data Web...

Casual Inference ·

2026/09/28

Follow Regression to the Mean Girls on [⁠⁠⁠Apple Podcasts⁠⁠⁠], [⁠⁠⁠Spotify⁠⁠⁠], or wherever you love to listen!⁠Emily Riederer⁠⁠⁠⁠⁠ (Capital One) joins ⁠⁠⁠⁠⁠⁠⁠Sarah Lotspeich⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠Lucy D'Agostino McGowan⁠⁠⁠⁠⁠⁠⁠⁠ with a data-driven side quest into scraping election data. Check out the full process here: ⁠⁠⁠⁠⁠⁠⁠regressiontothemeangirls.com⁠⁠⁠⁠⁠⁠⁠Follow along on instagram: ⁠⁠⁠⁠⁠@regressiontothemeangirls⁠⁠⁠

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00:39:46

Regression to the Mean Girls | The Art of Web...

Casual Inference ·

2026/09/21

Follow Regression to the Mean Girls on [⁠⁠Apple Podcasts⁠⁠], [⁠⁠Spotify⁠⁠], or wherever you love to listen!⁠Hadley Wickham⁠ (Posit) joins ⁠⁠⁠⁠⁠Sarah Lotspeich⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠Lucy D'Agostino McGowan⁠⁠⁠⁠⁠⁠ with a data-driven side quest into building a web scraper to help acquire art. Check out the full process here: ⁠⁠⁠⁠⁠regressiontothemeangirls.com⁠⁠⁠⁠⁠Links to content we chat about on the episode:⁠⁠⁠Weston Lambert Art⁠⁠Parallel strands between learning crochet and learning programming by Maëlle

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01:15:41

Regression to the Mean Girls | The Comfort of...

Casual Inference ·

2026/09/14

Follow Regression to the Mean Girls on [⁠Apple Podcasts⁠], [⁠Spotify⁠], or wherever you love to listen!Christine Zhang⁠ (⁠New York Times⁠) joins ⁠⁠⁠Sarah Lotspeich⁠⁠⁠ and ⁠⁠⁠Lucy D'Agostino McGowan⁠⁠⁠⁠ with a data-driven side quest into the TV show Law and Order. Check out the full analysis here: ⁠⁠⁠regressiontothemeangirls.com⁠⁠⁠Links to content we chat about on the episode:⁠Wrong Number newsletter⁠⁠The Speed of Justice, According to Law & Order⁠Follow along on

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00:42:02

Regression to the Mean Girls | The Work is Mysterious...

Casual Inference ·

2026/09/07

Follow Regression to the Mean Girls on [Apple Podcasts], [Spotify], or wherever you love to listen!⁠Lucy D'Agostino McGowan⁠⁠ and ⁠⁠Sarah Lotspeich⁠⁠ return with a data-driven side quest into the TV show Severance. Lucy analyzes whether Milchick really uses more complicated language than his colleagues.Check out the full analysis here: ⁠⁠regressiontothemeangirls.com⁠⁠Links to content we chat about on the episode:⁠⁠Lucy's Severance data R package [mdr]⁠⁠⁠⁠Lucy's

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00:56:48

Regression to the Mean Girls | Romance by the...

Casual Inference ·

2026/08/31

Sarah Lotspeich⁠ and ⁠Lucy D'Agostino McGowan⁠ introduce Regression to the Mean Girls, a podcast about data-driven side quests in pop culture. In this episode, Sarah analyzes 45 recent romance novels to find out how different romance tropes affect the timing of the couple’s first kiss.Check out the full analysis here: ⁠https://regressiontothemeangirls.com/⁠Links to content we chat about on the episode:⁠Edward L. Kaplan and the Kaplan-Meier Survival Curve⁠⁠Nonparametric Estimation from Incomplete

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52:55

Optimizing Data Workflows with Emily Riederer |...

Casual Inference ·

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.

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52:05

Combining Data & Making Effects Generalizable with...

Casual Inference ·

2025/06/12

Carly Brantner is an assistant professor of Biostatistics & Bioinformatics at Duke University and Duke Clinical Research Institute. 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.

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49:31

The Art of Clarity with Andrew Heiss | Season 6...

Casual Inference ·

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

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55:22

Study Critique: What Went Wrong and How We'd Do It...

Casual Inference ·

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

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45:20

From Model to Meaning with Vincent Arel-Bundock |...

Casual Inference ·

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

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01:22:09

Propensity Scores, R Packages, and Practical Advice...

Casual Inference ·

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.

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51:51

Causal Assumptions and Large Language Models | Season...

Casual Inference ·

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.

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44:48

Data Integration for Impact with Len Testa | Season 6...

Casual Inference ·

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

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48:12

Starting the Conversation on Models with Alyssa...

Casual Inference ·

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:

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38:57

Flexible methods with Edward Kennedy | Season 5...

Casual Inference ·

2024/06/26

Edward Kennedy Associate Professor, Department of Statistics & Data Science, Carnegie Mellon. Episode notes: 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

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49:43

What Sports and Feminism can tell us about Causal...

Casual Inference ·

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,

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51:43

Observational Causal Analyses with Erick Scott |...

Casual Inference ·

2024/05/29

Erick Scott is founder of cStructure, a causal science startup. Erick has expertise in medicine, public health, and computational biology. info@cStructure.io “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 McDadeEdited by Cameron Bopp

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59:07

Friends Let Friends Do Mediation Analysis with Nima...

Casual Inference ·

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

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48:23

Fun and Game(s) Theory with Aaditya Ramdas | Season 5...

Casual Inference ·

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

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