Ross GaylerLong HiveMind request: Guidelines for causal DAGs in Bayesian modelling
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#causal
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JMLR<p>'Optimal Experiment Design for Causal Effect Identification', by Sina Akbari, Jalal Etesami, Negar Kiyavash.</p><p><a href="http://jmlr.org/papers/v26/22-1516.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v26/22-1516.ht</span><span class="invisible">ml</span></a> <br> <br><a href="https://sigmoid.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://sigmoid.social/tags/algorithms" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>algorithms</span></a> <a href="https://sigmoid.social/tags/computational" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computational</span></a></p>
Dr. LabRat<p><span class="h-card" translate="no"><a href="https://fediscience.org/@DrYohanJohn" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>DrYohanJohn</span></a></span> Any idea on how to express <a href="https://fediscience.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> statements using differential equations? Their lack of directional assignment also makes it difficult to understand some generative models… (probably the purpose of the model is different)</p>
Dr Mircea Zloteanu ☀️ 🌊🌴<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statstab</span></a> #212 Eight basic rules for causal inference</p><p>Thoughts: Good explanation of the basics; confounder, colliders, randomization, and when to adjust.</p><p><a href="https://mastodon.social/tags/DAGs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DAGs</span></a> <a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causalinference</span></a> <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://mastodon.social/tags/collider" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>collider</span></a> <a href="https://mastodon.social/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a> <a href="https://mastodon.social/tags/r" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>r</span></a></p><p><a href="https://pedermisager.org/blog/seven_basic_rules_for_causal_inference/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pedermisager.org/blog/seven_ba</span><span class="invisible">sic_rules_for_causal_inference/</span></a></p>
Chris. Bart.<p>To fully realize the potential of our clinical trials, we must go beyond randomization, and use causal inference and pharmacometric modelling and simulation. Advancing both we show that non-linear mixed effects modelling implements the equivalent of standardization in causal inference. Dive into this if you're into <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causalinference</span></a> <a href="https://fosstodon.org/tags/DAGs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DAGs</span></a> <a href="https://fosstodon.org/tags/pharmacometrics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pharmacometrics</span></a>, or clinical development <a href="https://fosstodon.org/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a>.</p><p><a href="https://doi.org/10.1002/psp4.13239" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.1002/psp4.13239</span><span class="invisible"></span></a></p>
DSLC Videos<p>Recent <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>DSLC</span></a></span> club meetings:</p><p>:python: Practical Deep Learning for Coders: Chapter 16 "The Learner framework" <a href="https://youtu.be/e9VFOpn2hmA" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/e9VFOpn2hmA</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/PyData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyData</span></a> <a href="https://fosstodon.org/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a> <a href="https://fosstodon.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a></p><p>:rstats: The Effect: 15 "Simulation" <a href="https://youtu.be/amymCljDt3k" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/amymCljDt3k</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causality</span></a></p><p>:rstats: ggplot2: Elegant Graphics for Data Analysis: 15 "Coordinate systems" <a href="https://youtu.be/Ymj-4YggV3Q" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/Ymj-4YggV3Q</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/DataViz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataViz</span></a> <a href="https://fosstodon.org/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ggplot2</span></a></p><p>Subscribe at <a href="http://DSLC.video" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="">DSLC.video</span><span class="invisible"></span></a> for hours of <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> videos every week!</p>
DSLC Videos<p>Recent <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>DSLC</span></a></span> club meetings:</p><p>:rstats: ggplot2: Elegant Graphics for Data Analysis: 12 "Other aesthetics" <a href="https://youtu.be/NTuhL4pX7VQ" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/NTuhL4pX7VQ</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/DataViz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataViz</span></a> <a href="https://fosstodon.org/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ggplot2</span></a></p><p>:rstats: The Effect: 14 "Matching" <a href="https://youtu.be/XzsVGC8UYeo" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/XzsVGC8UYeo</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causality</span></a></p><p>:python: Practical Deep Learning for Coders: 13 "Backpropagation & MLP" <a href="https://youtu.be/EmKUrnYGZ04" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/EmKUrnYGZ04</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/PyData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyData</span></a> <a href="https://fosstodon.org/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a> <a href="https://fosstodon.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a></p><p>Subscribe at <a href="https://DSLC.video" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">DSLC.video</span><span class="invisible"></span></a> for hours of <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> videos every week!</p>
DSLC Videos<p>Recent <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>DSLC</span></a></span> club meetings:</p><p>:python: Practical Deep Learning for Coders: 10 "Diving Deeper" <a href="https://youtu.be/6_aM714giok" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/6_aM714giok</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/PyData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyData</span></a> <a href="https://fosstodon.org/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a> <a href="https://fosstodon.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a></p><p>:rstats: ggplot2: Elegant Graphics for Data Analysis: 10 "Position scales and axes" <a href="https://youtu.be/vaXDjSMhcx8" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/vaXDjSMhcx8</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/DataViz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataViz</span></a> <a href="https://fosstodon.org/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ggplot2</span></a></p><p>:rstats: The Effect: 10 "Treatment Effects" & Chapter 11 "Causality with Less Modeling" <a href="https://youtu.be/G3Ur3lZAYCs" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/G3Ur3lZAYCs</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causality</span></a></p><p>Subscribe at <a href="https://DSLC.video" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">DSLC.video</span><span class="invisible"></span></a> for hours of <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> videos every week!</p>
DSLC Videos<p>Recent <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>DSLC</span></a></span> club meetings:</p><p>:python: Practical Deep Learning for Coders: "Data ethics" <a href="https://youtu.be/rTkF5INDV5I" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/rTkF5INDV5I</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/PyData" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyData</span></a> <a href="https://fosstodon.org/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a> <a href="https://fosstodon.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> </p><p>:rstats: ggplot2: 7 "Networks" <a href="https://youtu.be/1uwJaDIzdCg" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/1uwJaDIzdCg</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/DataViz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataViz</span></a> <a href="https://fosstodon.org/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ggplot2</span></a> </p><p>:rstats: The Effect: Intro to Research Design and Causality: 8 "Causal Paths and Closing Back Doors" <a href="https://youtu.be/OUP-3Ut6Xno" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/OUP-3Ut6Xno</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causality</span></a> </p><p>Subscribe at <a href="https://DSLC.video" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">DSLC.video</span><span class="invisible"></span></a> for hours of <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> videos every week!</p><p>Participate at <a href="https://DSLC.io" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">DSLC.io</span><span class="invisible"></span></a></p>
DSLC Videos<p>Recent <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>DSLC</span></a></span> club meetings:</p><p>:rstats: Advanced R: Chapter 12 "Base types" <a href="https://youtu.be/6yW9dlErLv0" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/6yW9dlErLv0</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> </p><p>:rstats: The Effect: An Introduction to Research Design and Causality: Chapter 6 "Causal Diagrams" <a href="https://youtu.be/YIxO_L728mg" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/YIxO_L728mg</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causality</span></a> </p><p>:rstats: :python: Mastering Shiny: Chapter 11 "Bookmarking" <a href="https://youtu.be/g7M1n3jaA9c" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/g7M1n3jaA9c</span><span class="invisible"></span></a></p><p>Subscribe at <a href="https://DSLC.video" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">DSLC.video</span><span class="invisible"></span></a> for hours of <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> videos every week!</p><p>Participate in <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>DSLC</span></a></span> book clubs at <a href="https://DSLC.io" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">DSLC.io</span><span class="invisible"></span></a></p>
DSLC Videos<p>Recent <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>DSLC</span></a></span> club meetings:</p><p>:rstats: The Effect: An Introduction to Research Design and Causality: Chapter 0 "Introduction" <a href="https://youtu.be/dinQTPH7CqU" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/dinQTPH7CqU</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causality</span></a> </p><p>:rstats: ggplot2: Elegant Graphics for Data Analysis: Chapter 2 "First steps" <a href="https://youtu.be/uA8ZhSE29xY" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/uA8ZhSE29xY</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/DataViz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataViz</span></a> <a href="https://fosstodon.org/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ggplot2</span></a></p><p>Subscribe at <a href="https://DSLC.video" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">DSLC.video</span><span class="invisible"></span></a> for hours of <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> videos every week!</p><p>Participate in <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>DSLC</span></a></span> book clubs at <a href="https://DSLC.io" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">DSLC.io</span><span class="invisible"></span></a></p>
Dr. LabRat<p><span class="h-card" translate="no"><a href="https://fediscience.org/@UlrikeHahn" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>UlrikeHahn</span></a></span> I would love to see some proper work on the <a href="https://fediscience.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> impact of social media scale on toxicity (among other topics). While such overwhelming online hatred may be especially relevant for youngsters in their emotional development, things may even get worse when a deal of personal effort and hard work is at stake (also as an instance of small scale impact)… Any reading recommendations will be greatly appreciated!</p>
Dr. LabRat<p><span class="h-card" translate="no"><a href="https://scholar.social/@joakinen" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>joakinen</span></a></span> Also from this linked post, "(...) asking the right questions is one of the most important skills he’s learned", which is precisely the first step in <a href="https://fediscience.org/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causalinference</span></a>: ask a <a href="https://fediscience.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> question. The overlap between (computer science) <a href="https://fediscience.org/tags/engineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>engineering</span></a> and <a href="https://fediscience.org/tags/philosophy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>philosophy</span></a> through <a href="https://fediscience.org/tags/causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causality</span></a> may be one of the clearest examples of this needed change of mindset [1]. <span class="h-card" translate="no"><a href="https://mathstodon.xyz/@Jose_A_Alonso" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>Jose_A_Alonso</span></a></span></p><p>[1] <a href="https://cs.ulb.ac.be/conferences/ebiss2023/slides/EBISS2023_slides_JordiVitria_1.IntroCausality.pdf" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">cs.ulb.ac.be/conferences/ebiss</span><span class="invisible">2023/slides/EBISS2023_slides_JordiVitria_1.IntroCausality.pdf</span></a></p>
Jouni Helske<p>We're looking for a senior researcher for CAUSALTIME project at the INVEST Research Flagship Centre at the University of Turku starting 1/2024 at the earliest.<br> <br>The overall goal of the project is to develop new <a href="https://fediscience.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayesian</span></a> <a href="https://fediscience.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> inference methods for panel/time series data. Depending on your skills and interests, you can work on various methodological and/or applied social science research problems utilising the methods developed in the project.<br> <br>See more at <a href="https://ats.talentadore.com/apply/erikoistutkija-tai-vaitoskirjatutkija/8RbY22?lang=en&UTUID=200551" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">ats.talentadore.com/apply/erik</span><span class="invisible">oistutkija-tai-vaitoskirjatutkija/8RbY22?lang=en&UTUID=200551</span></a></p>
Will Lowe<p>My Spring <a href="https://colliderbias.net/tags/Causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Causal</span></a> course is looming and I'm looking for a Berlin-based teaching assistant. It's DAG-oriented and policy focused, with a touch of ML.</p><p>Each week is 2h of me going on about causal inference plus 4h of explaining to students what on earth I was trying to say. Labs are in <a href="https://colliderbias.net/tags/Rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Rstats</span></a> but Python might be helpful.</p><p>Note: Ceci n'est pas un cours d'économétrie -- we go everywhere, from sociology to policy, epidemiology, and economics. </p><p>Let me know if this sounds like 12 weeks of a good time</p>
Aki Vehtari<p>**Active Statistics** book by Andrew Gelman and I, coming in April, <a href="https://www.cambridge.org/highereducation/books/active-statistics/4E066112B3F82CA44C81CB4097960808" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">cambridge.org/highereducation/</span><span class="invisible">books/active-statistics/4E066112B3F82CA44C81CB4097960808</span></a> is full of **Stories, Games, Problems, and Hands-on Demonstrations for Applied Regression and Causal Inference.** The goal is to help build courses based on **Regression and Other Stories** <a href="https://avehtari.github.io/ROS-Examples/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">avehtari.github.io/ROS-Example</span><span class="invisible">s/</span></a> and give ideas for any statistics course</p><p><a href="https://bayes.club/tags/Bayes" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayes</span></a> <a href="https://bayes.club/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayesian</span></a> <a href="https://bayes.club/tags/teaching" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>teaching</span></a> <a href="https://bayes.club/tags/Regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Regression</span></a> <a href="https://bayes.club/tags/Causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Causal</span></a></p>
Ben Kanter<p><span class="h-card" translate="no"><a href="https://neuromatch.social/@elduvelle" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>elduvelle</span></a></span> <span class="h-card" translate="no"><a href="https://neuromatch.social/@NicoleCRust" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>NicoleCRust</span></a></span> <span class="h-card" translate="no"><a href="https://neuromatch.social/@vineettiruvadi" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>vineettiruvadi</span></a></span> </p><p>Here's a great quote for you to ponder when you have time, from Alicia Juarrero's new book <a href="https://neuromatch.social/tags/ContextChangesEverything" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ContextChangesEverything</span></a>:</p><p>"according to <a href="https://neuromatch.social/tags/reductionism" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>reductionism</span></a>, our intuition that mental processes such as intentions and beliefs have powers to actively bring about meaningful, purposive actions is illusory. Thoughts, feelings, and intentions derive their powers and properties from biology; biology from those of chemistry; chemistry from physics. Properties that appear unique to biological organisms (such as being alive) or human beings (such as symbolic language) can, in principle, be inferred from chemical processes that constitute them. <a href="https://neuromatch.social/tags/Causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Causal</span></a> powers that seem to issue from those higher-level properties can be derived from physical properties, at least in principle. It is not quite “turtles all the way down,” however. The turtle at the bottom (at the level of elementary physics) is special. The primary properties of a-toms, reality’s constituents (read now quarks and electrons), are the real and most simple stuff that does the causal work and provides <a href="https://neuromatch.social/tags/explanatory" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>explanatory</span></a> power. Ultimate causes reside in and issue from there. Meaning and purpose are impotent.</p><p>Such is the dream of a <a href="https://neuromatch.social/tags/theoryofeverything" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>theoryofeverything</span></a> , the promise of an equation that spells out the lawful correlations among microdetails and from which everything else can be derived and precisely predicted."</p>
Joseph A di Paolantonio<p>I also need to be researching and writing more about multi-modal <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://mastodon.social/tags/DigitalTwins" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DigitalTwins</span></a> in these contexts, especially with the advent of liquid neural networks <a href="https://mastodon.social/tags/LNN" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LNN</span></a> within the realm of truly <a href="https://mastodon.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayesian</span></a> neural networks using empirical Bayes and weighted Bayesian variables for a priori similarity of engineering and technical parameters expressed as directed acyclic graphs <a href="https://mastodon.social/tags/DAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DAG</span></a> within the digital twins of the <a href="https://mastodon.social/tags/SensAE" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SensAE</span></a> and interactions/dependencies of neighboring sensor analytics ecosystems</p>
Christos Argyropoulos MD, PhD<p>It is much more difficult to do an <a href="https://med-mastodon.com/tags/observational" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>observational</span></a> cohort study well vs. an RCT. People forget that the <a href="https://med-mastodon.com/tags/selection" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>selection</span></a> into cohort matters & that <a href="https://med-mastodon.com/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> assessments are more difficult (if outright impossible) with <a href="https://med-mastodon.com/tags/cohorts" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cohorts</span></a>, esp if you don't/won't grasp the mechanics of cohort generation.<br><a href="https://med-mastodon.com/tags/Biostatistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Biostatistics</span></a> <a href="https://med-mastodon.com/tags/epidemiology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>epidemiology</span></a> <a href="https://med-mastodon.com/tags/clinicalresearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>clinicalresearch</span></a> <a href="https://med-mastodon.com/tags/clinicaltrials" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>clinicaltrials</span></a></p>
Joseph A di Paolantonio<p>How will we continue to model ever evolving techniques? How might we include them in and <a href="https://mastodon.social/tags/SystemArchitecture" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SystemArchitecture</span></a> <a href="https://mastodon.social/tags/SystemsArchitecture" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SystemsArchitecture</span></a> and the concepts of sensor analytics ecosystems <a href="https://mastodon.social/tags/SensAE" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SensAE</span></a> and other ecosystems, especially in digital twins and <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://mastodon.social/tags/DigitalTwins" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DigitalTwins</span></a> <a href="https://mastodon.social/tags/CDT" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CDT</span></a></p>
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