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.

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 ears hurt.

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 talk about the episodes. It inspires me to challenge how I teach, and how I approach analyses theoretically and analytically. Thank you!

Drs. 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!

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 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.

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!

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 and stats to semi-laypeople like myself. My only critique is that the audio is pretty quiet, i hope the mic setup can improve.

I love this podcast. Lucy and Ellie help me find joy in learning causal inference and enjoy their sense of humor!

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!

This 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

Causal 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!

This 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.

Variety 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.

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”

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.

This podcast is a treasure. The hosts are amazing, the guests are insightful, and the topics are always interesting.

For all the people that work in the epidemiology field these are super interesting conversation

This 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.

Love the podcast. It wonderful that references to articles are provided. That is so helpful.

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!

Everyone 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.