Yishai Shimoni
@ShimoniYishai
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Joined October 2017
Proud to announce our recent publication: "Improving Inverse Probability Weighting by Post-calibrating" (journal of Epidemiology) https://t.co/zzPo5l0JaR The paper discusses the importance of the propensity to treat to reflect the actual probability to treat @ehudkar @RomGutman
journals.lww.com
e perform a simulation study to assess the error in estimating the average treatment effect before and after applying a simple and well-established postprocessing method to calibrate the propensity...
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Have you ever wondered: can I estimate ATE while being interpretable ? Check out this new blog post where I present the research I had the chance to do while interning at @IBMResearch. Thanks to my co-authors Liran Szlak, @ehudkar @cholmesuk @ShimoniYishai
https://t.co/UjGrFLzmty
medium.com
A model for interpretable estimation of population-level causal effect that can handle high-dimensional datasets.
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🚀 NEW Release: The IBM Causal Inference 360 Toolkit offers access to multiple tools that can move the decision-making processes from “best guess” to best data-based decisions. Learn how ➡️ https://t.co/2HvPfP8lD8
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Our modest contribution to COVID-19 analysis https://t.co/WsKjz7UUSJ via @weebly
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Check out how our recent #causal_inference #python library can help with method and model selection https://t.co/a5s67htBB1 via @weebly
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