Mathematics · Lucy D'Agostino McGowan and Ellie Murray
by Lucy D'Agostino McGowan and Ellie Murray
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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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 filler language were out-of-control distracting. It was hard to get through and focus on what was being said, which is was very interested in. If the hosts can work on minimizing the, “like, literally, whatever” 💁🏻♀️, then this would be a perfect podcast.
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.
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!
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