Modern science of making sense from greasy data
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“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!”
Grschunchibdseyv via Apple Podcasts ·
United States of America ·
06/05/24
More reviews of Casual Inference
“This podcast is a fun and engaging way to get exposure to current developments and debates in the world of causal inference. While the hosts come from a biostatistics and epidemiology perspective, as do some of the applications discussed, as a political scientist I’d say the show and methods...”Read full review »
AlanVancouver via Apple Podcasts ·
Canada ·
11/15/20
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“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.”
Doc_Claire via Apple Podcasts ·
United States of America ·
12/05/19
“Really awesome for anyone interested in causal inference, statistics, machine learning or epidemiology.”
Mozzquito via Apple Podcasts ·
Australia ·
06/23/21
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