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Prikvačeni tweet
For the first time ever, a full draft of the
#CausalInferenceBook is ready to download. Enjoy it and send us your comments. Please thank our publisher@RobCalver5 for supporting the free dissemination of the book. A print version (for purchase) is expected to follow in 2020.https://twitter.com/_MiguelHernan/status/1080036433182838784 …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Miguel Hernán proslijedio/la je Tweet
Who’s up for a
#tweetorial about adherence, per-protocol effects, and randomized trials?@_MiguelHernan and I have a new paper out in collaboration with the#CHARM trial team. Will this be the one that finally convinces you?
OA Link:https://authors.elsevier.com/a/1aUFF_ZI2ghdM- …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Miguel Hernán proslijedio/la je Tweet
Join
@HMSHCP faculty member José Zubizarreta on February 7 for a panel discussion on estimating the impact of a natural disaster on health outcomes using weighting for causal inference. This event is part of the Kolokotrones Symposium on Data Science at@HarvardChanSPH.pic.twitter.com/Q19tTy9o7p
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Competing events are responsible for much confusion in
#causalinference studies. What to do when some individuals die before developing the event of interest? If you were taught that censoring individuals who die is mandatory or that using hazards helps, please read our paper
https://twitter.com/JessGeraldYoung/status/1221855010650316800 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
An improved version of our
#CausaIInference#WhatIfBook can now be *freely* downloaded. Many thanks to all of you who reported typos and other issues. Scoop: This version includes the book cover illustrated by the inimitable Josh McKible (@mckibillo).pic.twitter.com/OhhUwhgjJ7
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Future authors: Good news. No more intrusive letters from me when submitting to the best health
#datascience journal. After 12 years, I'm bowing out as Editor of@EpidemiologyLWW. Congrats to new Editor Sonja Swanson! Thanks to@WilcoxEPID@TimothyLash et al for a great ride.pic.twitter.com/orYPdENXeF
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Miguel Hernán proslijedio/la je Tweet
I've seen so many people reference/tweet
@_MiguelHernan's seminal paper The Hazard of Hazard Ratios and, to my shame, only just got to reading it. 1/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3653612/ …pic.twitter.com/G9y9Dta6Yv
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Miguel Hernán proslijedio/la je Tweet
The idea that observational
#causalinference is an attempt to mimic a hypothetical experiment can be traced back to Dorn@AMJPublicHealth 1953 (43: 677-83). Earlier references anyone? Also, Cochran (Rubin's PhD advisor) was explicit about it in the 1960shttps://twitter.com/_MiguelHernan/status/941306115165372416 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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4/ Do you—like
@yudapearl below—prefer to express your#generalizability assumptions using causal diagrams? No problem. Led by Issa Dahabreh, here https://arxiv.org/abs/1906.10792 we use graphs to examine the conditions for generalizability of causal inferences from a#randomized trial.pic.twitter.com/w7v7dez3m2
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Miguel Hernán proslijedio/la je Tweet
As the editors of
@biostatistics,@drizopoulos and I are thrilled to share this free access multidisciplinary collection of commentaries on machine learning for causal inference. All 5 pieces are linked in our editorial about the series: https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxz045/5631847 …pic.twitter.com/zWbMOjT8Na
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3/ Want more? This article in
@Biometrics_ibs considers estimators to generalize inferences from individuals in randomized trials to all trial-eligible individuals: https://www.ncbi.nlm.nih.gov/pubmed/30488513 And this article in@EpidemiologyLWW clears some confusions: https://journals.lww.com/epidem/fulltext/2019/11000/On_the_Relation_Between_G_formula_and_Inverse.5.aspx …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
2/ For those interested in methods for extending inferences from randomized trials to a target population: Take a look at our tutorial https://arxiv.org/pdf/1805.00550.pdf … (soon to appear in Statistics in Medicine) You will find identification conditions AND three estimation approaches.pic.twitter.com/AkVr4Mjc3u
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1/ Suppose you want to extend causal inferences from a randomized trial to a target population. Is that
#transportability or#generalizability? Issa Dahabreh and I propose an answer in this brief commentary in the European Journal of Epidemiology: https://www.ncbi.nlm.nih.gov/pubmed/31218483 pic.twitter.com/QMS8HuVDag
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"Draw Your Assumptions Before Your Conclusions" Free
#CausalDiagramsCourse Data scientists of the world, registration is open for our online course on Causal Diagrams (version 2). Thanks to Joy Shi,@AprilOpoliner, and the team@HarvardOnline@edXOnlinehttps://www.edx.org/course/causal-diagrams-draw-your-assumptions-before-your-conclusions-2 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Miguel Hernán proslijedio/la je Tweet
My new course "Causal Data Science with Directed Acyclic Graphs" has finally been published at
@udemy. And I'm super excited to share it with you! https://www.udemy.com/course/causal-data-science/?referralCode=4FB57D92601437BEB794 …#CausalAI#Causality#MachineLearning#DataScience#Econometrics#DAG#MOOCPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
While we are on this topic, remember Brandolini's principle
pic.twitter.com/SGbAWXCxjs
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Anyone who thinks that criticizing is "Easy work" doesn't know how to criticize. Anyone who thinks that creating is always "Hard work" hasn't seen many human creations. Good science needs teams of creators and criticizers. Privileging either creation or criticism is not wise.https://twitter.com/ValaAfshar/status/1182272328685756417 …
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Miguel Hernán proslijedio/la je Tweet
Excited to share our research in
@NatureMedicine! Explicitly emulating a target trial reduces bias in analyses of electronic health records. An application to statins and cancer. https://rdcu.be/bTq6c@_MiguelHernan@SpirosDenaxas@xabieradrian Roger Loganpic.twitter.com/3WhuQXwRLn
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Please join us in publishing your failures. It is the best way to fight hype in
#causalinference from complex longitudinal data. Algorithms may help but, at the end of the day, either you do or don't have data on treatments, outcomes, and confounders. It is really that simple.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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