Joseph A di Paolantonio<p>While the URI points to a Wired article entitled to be about the <a href="https://mastodon.social/tags/Metaverse" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Metaverse</span></a> the article is actually about <a href="https://mastodon.social/tags/industrial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>industrial</span></a>, <a href="https://mastodon.social/tags/manufacturing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>manufacturing</span></a> and <a href="https://mastodon.social/tags/robotics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>robotics</span></a> use of <a href="https://mastodon.social/tags/DigitalTwins" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DigitalTwins</span></a> — that term is used frequently throughout the article. While LLM/GPT style <a href="https://mastodon.social/tags/generative" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>generative</span></a> technology is mentioned, imagine, as you read the article, <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> methods for multi-modal, multiple model composable, causal digital twins</p><p><a href="https://www.wired.com/story/the-metaverse-is-here-and-its-industrial/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">wired.com/story/the-metaverse-</span><span class="invisible">is-here-and-its-industrial/</span></a> </p><p><a href="https://mastodon.social/tags/SensorAnalyticsEcosystem" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SensorAnalyticsEcosystem</span></a> <a href="https://mastodon.social/tags/SensAE" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SensAE</span></a> <a href="https://mastodon.social/tags/IIoT" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>IIoT</span></a></p>
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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 noreferrer" 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 noreferrer" target="_blank">#<span>causal</span></a> <a href="https://sigmoid.social/tags/algorithms" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>algorithms</span></a> <a href="https://sigmoid.social/tags/computational" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>computational</span></a></p>
Joseph A di Paolantonio<p>We need to explore how these distributed <a href="https://mastodon.social/tags/microgrids" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>microgrids</span></a> can act as sensor analytics ecosystems <a href="https://mastodon.social/tags/SensAE" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SensAE</span></a> for the improvement of the local and global environments. As <a href="https://mastodon.social/tags/TeleInterActive" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TeleInterActive</span></a> Microgrids they can island, support neighbors and supplement regional grids. One way to do this will be through connected <a href="https://mastodon.social/tags/composable" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>composable</span></a> <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> <a href="https://mastodon.social/tags/DigitalTwins" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DigitalTwins</span></a> communicating, adding context and collaborating to bring resilience from the local to the regional level</p><p>3/3</p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #241 Mere Description</p><p>Thoughts: "causal arguments provide *explanation* while descriptive arguments provide *understanding*"</p><p>Sometimes description is good enough. </p><p><a href="https://mastodon.social/tags/description" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>description</span></a> <a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causalinference</span></a> <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> <a href="https://mastodon.social/tags/evidence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>evidence</span></a></p><p><a href="https://www.cambridge.org/core/journals/british-journal-of-political-science/article/mere-description/833643C6242D3A45D48BAAC3EF0C33D0" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">cambridge.org/core/journals/br</span><span class="invisible">itish-journal-of-political-science/article/mere-description/833643C6242D3A45D48BAAC3EF0C33D0</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 noreferrer" 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 noreferrer" 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 noreferrer" target="_blank">#<span>statstab</span></a> #231 Sample Splitting for Valid Powerful Design of Observational Studies</p><p>Thoughts: Observational studies are complicated things (more than many will admit). But, maybe there is a way forward (by copying ML!)</p><p><a href="https://mastodon.social/tags/observational" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>observational</span></a> <a href="https://mastodon.social/tags/bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bias</span></a> <a href="https://mastodon.social/tags/research" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>research</span></a> <a href="https://mastodon.social/tags/methodology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>methodology</span></a> <a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causalinference</span></a> <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> </p><p><a href="https://arxiv.org/abs/2406.00866" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2406.00866</span><span class="invisible"></span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" 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 noreferrer" target="_blank">#<span>DAGs</span></a> <a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causalinference</span></a> <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> <a href="https://mastodon.social/tags/collider" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>collider</span></a> <a href="https://mastodon.social/tags/correlation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correlation</span></a> <a href="https://mastodon.social/tags/r" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>r</span></a></p><p><a href="https://pedermisager.org/blog/seven_basic_rules_for_causal_inference/" rel="nofollow noopener noreferrer" 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 noreferrer" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causalinference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causalinference</span></a> <a href="https://fosstodon.org/tags/DAGs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DAGs</span></a> <a href="https://fosstodon.org/tags/pharmacometrics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pharmacometrics</span></a>, or clinical development <a href="https://fosstodon.org/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a>.</p><p><a href="https://doi.org/10.1002/psp4.13239" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.1002/psp4.13239</span><span class="invisible"></span></a></p>
Dr Mircea Zloteanu 🌼🐝<p><a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statstab</span></a> #209 Limitations of empirical calibration of p-values using observational data</p><p>Thoughts: Obs. research doesn't need p-values (imo) but ppl keep tryin to make'em happen</p><p><a href="https://mastodon.social/tags/pvalues" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pvalues</span></a> <a href="https://mastodon.social/tags/observational" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>observational</span></a> <a href="https://mastodon.social/tags/empirical" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>empirical</span></a> <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a></p><p><a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC5012943/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pmc.ncbi.nlm.nih.gov/articles/</span><span class="invisible">PMC5012943/</span></a><br>rebuttal<br><a href="https://pubmed.ncbi.nlm.nih.gov/27592566/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">pubmed.ncbi.nlm.nih.gov/275925</span><span class="invisible">66/</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 noreferrer" target="_blank">@<span>DSLC</span></a></span> club meetings:</p><p>:python: Practical Deep Learning for Coders: 12 "Mean shift clustering" <a href="https://youtu.be/sNiQh4IxbIU" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/sNiQh4IxbIU</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/PyData" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyData</span></a> <a href="https://fosstodon.org/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DeepLearning</span></a> <a href="https://fosstodon.org/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a></p><p>:rstats: The Effect: 13 "Regression" <a href="https://youtu.be/_3fPJ1xUdNw" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/_3fPJ1xUdNw</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causality" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causality</span></a></p><p>From the <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>DSLC</span></a></span> :rstats:chives:</p><p>:rstats: "R for Data Science: Relational data Part 1 (r4ds05 13)" <a href="https://youtu.be/4Ju9nj82Ksk" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/4Ju9nj82Ksk</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/R4DS" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>R4DS</span></a> </p><p>Subscribe at <a href="https://DSLC.video" rel="nofollow noopener noreferrer" 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 noreferrer" 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 noreferrer" target="_blank">@<span>DSLC</span></a></span> club meetings:</p><p>:python: Practical Deep Learning for Coders: 9 "Stable Diffusion" <a href="https://youtu.be/cBE-S7jNlKM" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/cBE-S7jNlKM</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/PyData" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyData</span></a> <a href="https://fosstodon.org/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DeepLearning</span></a> <a href="https://fosstodon.org/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> </p><p>:rstats: ggplot2: Elegant Graphics for Data Analysis: 9 "Arranging plots" <a href="https://youtu.be/bJCmUCJsdIA" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/bJCmUCJsdIA</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/DataViz" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataViz</span></a> <a href="https://fosstodon.org/tags/ggplot2" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ggplot2</span></a> </p><p>:rstats: The Effect: An Intro to Research Design and Causality: 9 "Finding Front Doors" <a href="https://youtu.be/8RJxoOz2dyg" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/8RJxoOz2dyg</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RStats</span></a> <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causality" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causality</span></a> </p><p>Subscribe at <a href="https://DSLC.video" rel="nofollow noopener noreferrer" 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 noreferrer" 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 noreferrer" target="_blank">@<span>DSLC</span></a></span> club meetings:</p><p>:rstats: The Effect 1 "Designing Research" & 2 "Research Questions" <a href="https://youtu.be/x_sywOkcxrA" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/x_sywOkcxrA</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> <a href="https://fosstodon.org/tags/causality" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causality</span></a></p><p>:rstats: <a href="https://fosstodon.org/tags/ggplot2" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ggplot2</span></a> 3 "Individual geoms" <a href="https://youtu.be/qhQrd0iZz_8" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/qhQrd0iZz_8</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/DataViz" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataViz</span></a> </p><p>:python: Practical Deep Learning for Coders 5 "From-scratch model" <a href="https://youtu.be/GfyZJO58y7Y" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">youtu.be/GfyZJO58y7Y</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/PyData" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyData</span></a> <a href="https://fosstodon.org/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DeepLearning</span></a> <a href="https://fosstodon.org/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> </p><p>Subscribe at <a href="https://DSLC.video" rel="nofollow noopener noreferrer" 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 noreferrer" target="_blank">#<span>DataScience</span></a> videos every week! <a href="https://fosstodon.org/tags/PyData" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyData</span></a></p><p>Participate in <span class="h-card" translate="no"><a href="https://fosstodon.org/@DSLC" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>DSLC</span></a></span> book clubs at <a href="https://DSLC.io" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">DSLC.io</span><span class="invisible"></span></a></p><p><a href="https://fosstodon.org/tags/RStats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RStats</span></a></p>
Joseph A di Paolantonio<p><span class="h-card" translate="no"><a href="https://mastodon.world/@rrtucci" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>rrtucci</span></a></span> This link?</p><p><a href="https://github.com/rrtucci/mappa_mundi" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">github.com/rrtucci/mappa_mundi</span><span class="invisible"></span></a></p><p>Cc: <span class="h-card" translate="no"><a href="https://mastodon.social/@Cmastication" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>Cmastication</span></a></span> </p><p><a href="https://mastodon.social/tags/Causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Causal</span></a> <a href="https://mastodon.social/tags/DAG" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DAG</span></a> <a href="https://mastodon.social/tags/MappaMundi" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MappaMundi</span></a> <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</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 noreferrer" 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 noreferrer" 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 noreferrer" 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 noreferrer" target="_blank">#<span>causalinference</span></a>: ask a <a href="https://fediscience.org/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" 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 noreferrer" target="_blank">#<span>engineering</span></a> and <a href="https://fediscience.org/tags/philosophy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>philosophy</span></a> through <a href="https://fediscience.org/tags/causality" class="mention hashtag" rel="nofollow noopener noreferrer" 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 noreferrer" 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 noreferrer" 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>
Joseph A di Paolantonio<p>We moved to <a href="https://mastodon.social/tags/Sustainability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Sustainability</span></a> with Shyam Varan Nath discussing the use of <a href="https://mastodon.social/tags/IoT" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>IoT</span></a> <a href="https://mastodon.social/tags/DigitalTwins" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DigitalTwins</span></a> for optimizing the use of <a href="https://mastodon.social/tags/renewableEnergy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>renewableEnergy</span></a> especially wind turbines, in <a href="https://mastodon.social/tags/SmartRegion" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SmartRegion</span></a> sensor analytics ecosystems <a href="https://mastodon.social/tags/SensAE" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SensAE</span></a> The discussion also considered <a href="https://mastodon.social/tags/Causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Causal</span></a> Digital Twins, the use of <a href="https://mastodon.social/tags/TeleInterActive" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TeleInterActive</span></a> <a href="https://mastodon.social/tags/Microgrids" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Microgrids</span></a> and the dependencies of sustainability and <a href="https://mastodon.social/tags/Security" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Security</span></a> in urban settings <a href="https://mastodon.social/tags/InternetOfThings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>InternetOfThings</span></a> <a href="https://mastodon.social/tags/IoTday" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>IoTday</span></a> <a href="https://mastodon.social/tags/IoTday2024" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>IoTday2024</span></a> <a href="https://mastodon.social/tags/AIIoTforGood" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIIoTforGood</span></a> <a href="https://mastodon.social/tags/Tech4Good" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Tech4Good</span></a> 7/16</p>
Chris Arnold<p>Stumbled over this pretty cool living bibliography on causal textanalysis. Might be useful to some. <a href="https://sigmoid.social/tags/textasdata" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>textasdata</span></a> <a href="https://sigmoid.social/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> <a href="https://sigmoid.social/tags/nlp" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>nlp</span></a> <a href="https://sigmoid.social/tags/sciencerocks" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sciencerocks</span></a> <a href="https://github.com/causaltext/causal-text-papers" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/causaltext/causal-t</span><span class="invisible">ext-papers</span></a></p>
Will Lowe<p>My Spring <a href="https://colliderbias.net/tags/Causal" class="mention hashtag" rel="nofollow noopener noreferrer" 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 noreferrer" 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 noreferrer" 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 noreferrer" 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 noreferrer" target="_blank">#<span>Bayes</span></a> <a href="https://bayes.club/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> <a href="https://bayes.club/tags/teaching" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>teaching</span></a> <a href="https://bayes.club/tags/Regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Regression</span></a> <a href="https://bayes.club/tags/Causal" class="mention hashtag" rel="nofollow noopener noreferrer" 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 noreferrer" 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 noreferrer" 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 noreferrer" 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 noreferrer" target="_blank">#<span>ContextChangesEverything</span></a>:</p><p>"according to <a href="https://neuromatch.social/tags/reductionism" class="mention hashtag" rel="nofollow noopener noreferrer" 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 noreferrer" 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 noreferrer" 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 noreferrer" 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>
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