Recent Episodes
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From Model to Meaning with Vincent Arel-Bundock | Season 6 Episode 4
Apr 24, 2025 – 45:20 -
Propensity Scores, R Packages, and Practical Advice with Noah Greifer | Season 6 Episode 3
Apr 10, 2025 – 01:22:09 -
Causal Assumptions and Large Language Models | Season 6 Episode 2
Mar 27, 2025 – 51:51 -
Data Integration for Impact with Len Testa | Season 6 Episode 1
Feb 28, 2025 – 44:48 -
Starting the Conversation on Models with Alyssa Bilinski | Season 5 Episode 11
Jul 10, 2024 – 48:12 -
Flexible methods with Edward Kennedy | Season 5 Episode 10
Jun 26, 2024 – 38:57 -
What Sports and Feminism can tell us about Causal Inference with Sheree Bekker & Stephen Mumford | Season 5 Episode 9
Jun 12, 2024 – 49:43 -
Observational Causal Analyses with Erick Scott | Season 5 Episode 8
May 29, 2024 – 51:43 -
Friends Let Friends Do Mediation Analysis with Nima Hejazi | Season 5 Episode 7
May 16, 2024 – 59:07 -
Fun and Game(s) Theory with Aaditya Ramdas | Season 5 Episode 6
May 1, 2024 – 48:23 -
Cookies, Causal Inference, and Careers with Ingrid Giesinger #Epicookiechallenge | Season 5 Episode 5
Apr 17, 2024 – 46:49 -
Analyzing the Analysts: Reproducibility with Nick Huntington-Klein | Season 5 Episode 4
Apr 3, 2024 – 45:44 -
Immortal Time Bias | Season 5 Episode 3
Mar 20, 2024 – 34:37 -
Targeted Learning with Mar van der Laan | Season 5 Episode 2
Mar 6, 2024 – 51:21 -
Pros and Cons of Randomized Controlled Trials | Season 5 Episode 1
Feb 21, 2024 – 17:55 -
Remembering Ralph B. D'Agostino, Sr.
Oct 2, 2023 – 49:37 -
Evidence Science with Cat Hicks | Season 4 Episode 11
Jul 17, 2023 – 49:41 -
M-Bias: Much Ado About Nothing? | Season 4 Episode 10
Apr 24, 2023 – 38:55 -
Thinking about Targeted Learning | Season 4 Episode 9
Apr 11, 2023 – 46:02 -
Prevention Strategies via the #Epicookiechallenge | Season 4 Episode 8
Mar 29, 2023 – 38:12 -
Sensitivity Analyses for Unmeasured Confounders | Season 4 Episode 7
Mar 14, 2023 – 38:46 -
Randomized Controlled Trials: Efficacy versus Effectiveness, Safety vs Safetiness | Season 4 Episode 6
Feb 28, 2023 – 01:07:25 -
The Value of Instrumental Variables with Maria Glymour | Season 4 Episode 5
Dec 10, 2022 – 01:00:22 -
Methods chat about personalized medicine and positivity in causal inference | Season 4 Episode 4
Nov 30, 2022 – 51:43 -
Hot takes and logistic regression love with Travis Gerke | Season 4 Episode 3
Nov 16, 2022 – 54:20 -
Counterfactual Thinking: Biomarkers, Napster, and Ice-T | Season 4 Episode 2
Nov 4, 2022 – 57:49 -
Population and Biomedical Data Science with Enrique Schisterman | Season 4 Episode 1
Oct 13, 2022 – 54:51 -
What is the value of a p-value with Charlie Poole and Chuck Scales | Season 3 Episode 13
May 3, 2022 – 01:16:11 -
It Depends with Sander Greenland | Season 3 Episode 12
Apr 18, 2022 – 01:27:55 -
The Intersection of Industrial Engineering and Causal Inference with Toyya Pujol | Season 3 Episode 11
Apr 5, 2022 – 01:04:35 -
The Intersection of Machine Learning and Causal Inference with Maggie Makar | Season 3 Episode 10
Mar 14, 2022 – 53:52 -
Artificial Intelligence, Personalized Medicine, and Causal Bounds with Judea Pearl | Season 3 Episode 9
Feb 28, 2022 – 55:04 -
The history of John Snow, Cholera, and Cookies with Chris Schaich | Season 3 Episode 8
Feb 14, 2022 – 48:45 -
Asking questions that matter, getting answers that help | Season 3 Episode 7
Dec 5, 2021 – 01:11:47 -
A Casual Look at Causal Inference History | Season 3 Episode 6
Nov 22, 2021 – 01:12:06 -
Hanging out in the data science trough of disillusionment with Hilary Parker | Season 3 Episode 5
Nov 8, 2021 – 01:09:42 -
Metascience with Noah Haber | Season 3 Episode 4
Oct 25, 2021 – 01:08:13 -
Solving Optimization Problems in Healthcare and Disney Theme Parks with Len Testa | Season 3 Episode 3
Oct 11, 2021 – 57:57 -
Causal Inference and Network Science for Public Health with Ashley Buchanan | Season 3 Episode 2
Sep 27, 2021 – 59:48 -
Coronavirus Rapid Tests Sensitivity, Specificity, Messaging, and Use Cases | Season 3 Episode 1
Sep 13, 2021 – 01:01:50 -
Our Michael Jordan Episode | Season 2 Episode 5
Mar 2, 2021 – 38:51 -
Health Policy with Julia Raifman | Season 2 Episode 4
Feb 8, 2021 – 01:05:40 -
Celebrating 100 years with a look forwards and back with the D'Agostinos | Season 2 Episode 3
Jan 21, 2021 – 01:02:56 -
The Most Ambitious Crossover | Season 2 Episode 2
Dec 15, 2020 – 52:25 -
Happy Anniversary to Us! | Season 2 Episode 1
Nov 13, 2020 – 54:49 -
Why Everyone is Excited About Causal Inference These Days with Roger Peng | Episode 18
Oct 30, 2020 – 59:27 -
Thinking About Schools Reopening From a Causal Perspective with Emily Oster | Episode 17
Oct 16, 2020 – 01:14:51 -
An Ode to Generalized Linear Models | Episode 16
Oct 2, 2020 – 45:55 -
Methodological Advances in Causal Inference with Betsy Ogburn | Episode 15
Sep 17, 2020 – 01:14:53 -
Casual Inference Live from SER | Episode 14
Jun 24, 2020 – 50:35
Recent Reviews
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kkflo00177Casual 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 that you devote to sharing your (and others’) expertise in this awesome podcast. Second (from this Linklater-trained sometime-performer, now translational almost-PhD, modeling enthusiast, chronically under-slept parent, here): GUYS! Guys (or girls). Lay off the vocal critiques. Women scientists get disproportionately scrutinized for anything other than their science knowledge in *all* *their* *spaces*, so please give it a rest. Female scientists do communicate differently, and vocal routing is merely a dimension of difference (see what I did just there with that non-deficit-based language?) Notwithstanding that this irrelevant-critique-as-authoritative-discourse approach is nothing new nor unique to Dr.’s Murray and D’Agastino-McGowan, it is an unnecessary criticism that falls squarely into the the “non-useful to others” feedback bin. This podcast is awesome, these scientists know their stuff, and its fun to listen to, full stop.
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MelFierrosVocal FryLove 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 ears hurt.
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Bill JesdaleThoughtful yet Approachable DiveCasual Inference is a thoughtful yet approachable dive into contemporary issues in epi, I recommend it to my students, and the faculty here love to talk about the episodes. It inspires me to challenge how I teach, and how I approach analyses theoretically and analytically. Thank you!
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GrschunchibdseyvModern science of making sense from greasy dataDrs. Murray and D’Agostino-Gowan provide the content that reflects the state of the art in the relatively recent interdisciplinary area of scientific methodology called causal inference. This would not be your first podcast on statistics; it has to be layered on top of a graduate degree in statistics, data science, epidemiology, public health, economics, quantitative social sciences, and the like. As I try to stay current and relevant in my own work (which is a different area of statistics), the podcast has been very helpful for me in getting a glimpse of the discipline where 90% of the current knowledge has been generated after I got my terminal degree (2005). Looking forward to new episodes, and keep doing great work!
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NickiMinaGreat conceptually but vocal fry undermines experience as a listenerHighly interested in the episodes and guests but unfortunately find it grating to listen to. Many people do not have natural speaking voices that are well suited for podcasts or radio (myself included). That said, news anchors are a prime example of our ability to improve our speaking voices. The hosts are clearly bright and have great energy, but could benefit from investing in vocal training in the interest of expanding their audience.
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breggurnsExcellent 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 driving to work!
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Pete AmerGreat podcastThis is a really fun and informative podcast on causal inference and data science. The hosts both are great at communicating topics in research design and stats to semi-laypeople like myself. My only critique is that the audio is pretty quiet, i hope the mic setup can improve.
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Dylan_PeteGreat 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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Crnichols423A must-listen!I love this podcast. It’s helped me tremendously throughout my first two years of my PhD program, leading up to my comprehensive exams. I find the way Ellie and Lucy explain things to be so clear and memorable. I’ve listened to some other epi/stats podcasts, but always prefer this one! Thank you Ellie and Lucy!
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havyn:)MathThis is so good!!!! I am 14 and really enjoy math and science I have always tried to ask my teachers to teach me but I never understand this is epic
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Data Science GirlData Science & Causal InferenceCausal Inference is a topic that is way underrepresented in data science content for how important it is. This podcast is epidemiology-focused, but data science practitioners will find it fun and insightful as well. The hosts are very sweet and fun. So glad this show is back!
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CuriosityUnboundEpisodes are too long by 2 inexperienced hostsThis podcast has potential but the 2 hosts are just annoying and the episodes too long. Every once in a while the show is punctuated by the shrill laughter from a host. They may be having fun but not the listeners. On a podcast you want to make every word count.
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Sherman DornFriendly and highly informativeVariety show about causal inference? Pretty close! Interviews, news, occasional teaching segments, Q&A, and only missing the musical numbers. More seriously, the hosts are ecumenical abut the different disciplinary approaches to inference, keep things relatively nontechnical, and fun.
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izzykaLove this show!The hosts are very smart, know a lot about epidemiology, biostatistics, and causal inference, and have a lot of fun! They have very good guests on. Extra props for the focus on “keeping it casual”
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JosephWesleyFun, insightful and highly engaging conversations with field experts.This is a great podcast for people new to causal inference as well as those helping to advance the methods. It’s ‘casual’ and guests are buzzed if they introduce words or concepts that need definition. The hosts interview leaders in the field and the conversations are always fun and insightful. Podcasting at its best.
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Shooter6789Cooler than coolThis podcast is a treasure. The hosts are amazing, the guests are insightful, and the topics are always interesting.
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O melhor ouvinteExcelent podcastFor all the people that work in the epidemiology field these are super interesting conversation
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xapata83FantasticThis is an absolutely fantastic podcast by experts in the field, featuring great guests who are also pioneers in the field. It’s a great “casual” introduction to a lot of important statistical techniques.
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NotKGApproachable and informativeLove the podcast. It wonderful that references to articles are provided. That is so helpful.
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foobar_stats3 things..1. Great intro music. 2. Great name and logo. 3. I like that they keep it light! It actually feels like a conversation at a bar...about stats! I love listening to this on my walk to work. Keep em coming!
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Doc_ClaireOutstandingEveryone working in epidemiology, biostatistics, or data science should be listening to this podcast. The hosts address state of the art methods in a lighthearted and approachable manner. Kudos to the American Journal of Epidemiology for sponsoring this great content.
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