Marshall Poe
Marshall Poe
Scott Cunningham, "Causal Inference: The Mixtape" (Yale UP, 2021)
Scott Cunningham, "Causal Inference: The Mixtape" (Yale UP, 2021)
Scott Cunningham, "Causal Inference: The Mixtape" (Yale UP, 2021)
Friday 19th November 2021
Scott Cunningham explains causal inference and its application in policy evaluation in this enlightening episode of 'New Books in Drugs, Addiction and Recovery'.
68 minutes
Informative
Thought-provoking
Engaging
Educational
Eye-opening
About this podcast
Author:
Marshall Poe
Overview:
Categories:
Understanding Addiction & Recovery
Navigating Alcohol Dependency
Navigating Intimate Relationships
Family Recovery from Addiction
Relationships & Community
Links:
Visit site
Episodes:
232 (View all)
Do you want to link to this podcast?
Get the buttons here!Scott Cunningham's Guide to Understanding Policy Impacts with Causal Inference
Causal inference is really all about using prior knowledge in a way to coach that... to figure out how to make it tell stories, you have to learn, you know where it's, what its languages, and you've got to learn you know how to how to interact with the data in a way that brings out the stories that are in there.
Ever scratched your head over the difference between correlation and causation? Scott Cunningham's book, *Causal Inference: The Mixtape*, might just be the answer you've been looking for. In this episode of 'New Books in Drugs, Addiction and Recovery', host Marshall Poe chats with Cunningham, an economics professor at Baylor University, about his accessible guide to understanding causal relationships in real-world data. Cunningham breaks down complex statistical methods into easy-to-grasp concepts, making them accessible even if you're not a math whiz.
He explains how these techniques can help us better understand the effects of various policy interventions. Imagine being able to evaluate the impact of legalized prostitution on public health or the effectiveness of mask mandates during the COVID-19 pandemic with a clearer lens. That's what causal inference offers. The conversation dives into why traditional randomized controlled trials aren't always feasible and how observational data can still provide valuable insights.
Cunningham shares examples from his own research on topics like mental healthcare, sex work, abortion, and drug policy, showing how these methods can be applied across different fields. If you're an economist, a social scientist, or just someone curious about how data can tell compelling stories, this episode is a treasure trove of knowledge. Cunningham's approach emphasizes using existing knowledge to uncover the narratives hidden within data, making complex theories relatable and practical.
Tune in to learn how you can start interacting with data in a way that brings out the stories waiting to be told. Whether you're looking to enhance your research skills or simply want to understand the world a bit better, this episode offers valuable insights into the art and science of causal inference.