Aleksander Molak’s Post

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Author of "Causal Inference & Discovery in Python" || Host at CausalBanditsPodcast.com || Causal AI for Everyone || Consulting & Advisory

Causality X Health? What's the biggest challenge in scaling causal inference models? On Monday, join me and my guest, Ehud Karavani (IBM Research), for the premiere of our conversation on causality in health. Ehud will share the experiences and challenges he faced on his causal journey. We will talk about Causallib - an open source Python causal inference library that Ehud created and people who impacted his career. Stay tuned for the premiere link on Monday or subscribe to get the link straight in your inbox: https://bit.ly/3z9rkGa With ❤️ from Causal Bandits Podcast

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Jason Gantenberg

Research Scientist & Assistant Professor (Practice) at Brown University School of Public Health

5mo

In epidemiology, I would say the biggest problem is data quality. For many problems, the benefit provided by flexible estimation methods over simple statistical models (e.g., to estimate nuisance parameters) is marginal. I suspect in many cases it's because the quality of the data is too low and that measurement error becomes the most significant impediment to identification.

Justin Bélair

Biostatistician in Science & Tech | Consultant | Author of Causal Inference in Statistics | Founder & Editor @ biostatistics.ca

6mo

Causal Inference in health!? I'm looking forward to this one :)

Joe H ☆

Dog Dad | Data Science, AI, ML, Semantic Knowledge Graphs, Computer Vision

6mo
Or Rimoch

Data Scientist at Dynamic Yield

6mo
Iyar Lin

Data science lead at Loops

6mo

Looking forward!

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