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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>
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>
Paul Balduf<p>Our <a href="https://mathstodon.xyz/tags/workshop" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>workshop</span></a> on <a href="https://mathstodon.xyz/tags/combinatorics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>combinatorics</span></a> in fundamental <a href="https://mathstodon.xyz/tags/physics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>physics</span></a> has three topic days, November 26-28.</p><p>Day 1: Random Geometry for <a href="https://mathstodon.xyz/tags/Quantum" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Quantum</span></a> <a href="https://mathstodon.xyz/tags/Gravity" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Gravity</span></a></p><p>Random geometry is a powerful mathematical framework for studying quantum gravity by modeling it as a statistical physics system, where each Boltzmann configuration corresponds to a spacetime geometry selected from an ensemble with a well-defined probability measure. When considering quantum gravity from a lattice perspective, where spacetime is discrete, the challenge of defining a suitable probability measure becomes a combinatorial problem. </p><p>Day 2: <a href="https://mathstodon.xyz/tags/Causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Causal</span></a> Set Theory</p><p>Causal Set Theory is an approach to Quantum Gravity in which spacetime is fundamentally discrete and takes the form of a locally finite partial order, or causal set. The twin questions leading much of the research in this field are: How does the continuum physics of General Relativity arise from an underlying discreteness? And what is the quantum nature of a discrete and dynamical spacetime? <a href="https://mathstodon.xyz/tags/causalset" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causalset</span></a></p><p>Day 3: Combinatorics in Perturbative <a href="https://mathstodon.xyz/tags/QFT" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>QFT</span></a> </p><p>A unique feature of quantum field theory is the central role that combinatorics plays: from generating Feynman graphs of scalar models to combinatorial maps and higher graph-like objects of sophisticated frameworks such as matrix-/tensor- and group field theories to renormalization Hopf algebras, and the theory of resurgence and asymptotic power series, to name but a few. The focus of this workshop will be on matrix-/tensor-/group-field theories and graph complexes. </p><p>Registration is open for everyone, and of course, free of charge.<br><a href="https://indico.mitp.uni-mainz.de/event/422/registrations/245/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">indico.mitp.uni-mainz.de/event</span><span class="invisible">/422/registrations/245/</span></a></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>
Joseph A di Paolantonio<p>We moved to <a href="https://mastodon.social/tags/Sustainability" class="mention hashtag" rel="nofollow noopener" 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" target="_blank">#<span>IoT</span></a> <a href="https://mastodon.social/tags/DigitalTwins" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DigitalTwins</span></a> for optimizing the use of <a href="https://mastodon.social/tags/renewableEnergy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>renewableEnergy</span></a> especially wind turbines, in <a href="https://mastodon.social/tags/SmartRegion" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SmartRegion</span></a> sensor analytics ecosystems <a href="https://mastodon.social/tags/SensAE" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SensAE</span></a> The discussion also considered <a href="https://mastodon.social/tags/Causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Causal</span></a> Digital Twins, the use of <a href="https://mastodon.social/tags/TeleInterActive" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TeleInterActive</span></a> <a href="https://mastodon.social/tags/Microgrids" class="mention hashtag" rel="nofollow noopener" 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" target="_blank">#<span>Security</span></a> in urban settings <a href="https://mastodon.social/tags/InternetOfThings" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>InternetOfThings</span></a> <a href="https://mastodon.social/tags/IoTday" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>IoTday</span></a> <a href="https://mastodon.social/tags/IoTday2024" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>IoTday2024</span></a> <a href="https://mastodon.social/tags/AIIoTforGood" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIIoTforGood</span></a> <a href="https://mastodon.social/tags/Tech4Good" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Tech4Good</span></a> 7/n</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>
Léo Varnet<p>Tired of these studies overstating the results by confusing <a href="https://qoto.org/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a> and <a href="https://qoto.org/tags/causation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causation</span></a>. A recent <a href="https://qoto.org/tags/PNAS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PNAS</span></a> paper (co-authored by D. Kahneman!) starts with the question "can money buy happiness?"... and does no offer any evidence of a <a href="https://qoto.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> relationship <a href="https://statmodeling.stat.columbia.edu/2023/08/01/studying-average-associations-between-income-and-survey-responses-on-happiness-be-careful-about-deterministic-and-causal-interpretations-that-are-not-supported-by-these-data/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statmodeling.stat.columbia.edu</span><span class="invisible">/2023/08/01/studying-average-associations-between-income-and-survey-responses-on-happiness-be-careful-about-deterministic-and-causal-interpretations-that-are-not-supported-by-these-data/</span></a> <a href="https://qoto.org/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://qoto.org/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://qoto.org/tags/correlationisnotcausation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlationisnotcausation</span></a></p>
Vincent Traag<p>Nature recently introduced new guidelines for studies engaging with questions of race, ethnicity or gender. Among others, it asks researchers to explain how they controlled for <a href="https://social.cwts.nl/tags/confounding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>confounding</span></a> variables. We agree this is important, especially because we think <a href="https://social.cwts.nl/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</span></a> and <a href="https://social.cwts.nl/tags/disparity" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>disparity</span></a> should be understood in <a href="https://social.cwts.nl/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> terms. In this <a href="https://social.cwts.nl/tags/LSE" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LSE</span></a> impact blog, <span class="h-card"><a href="https://social.cwts.nl/@LudoWaltman" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>LudoWaltman</span></a></span> and I discuss some of the challenges around this.</p><p><a href="https://blogs.lse.ac.uk/impactofsocialsciences/2023/05/25/the-bias-puzzle-understanding-gender-differences-in-academia/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">blogs.lse.ac.uk/impactofsocial</span><span class="invisible">sciences/2023/05/25/the-bias-puzzle-understanding-gender-differences-in-academia/</span></a></p>
Published papers at TMLR<p>Identifying Causal Structure in Dynamical Systems</p><p>Dominik Baumann, Friedrich Solowjow, Karl Henrik Johansson, Sebastian Trimpe</p><p><a href="https://openreview.net/forum?id=X2BodlyLvT" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=X2Bodl</span><span class="invisible">yLvT</span></a></p><p><a href="https://sigmoid.social/tags/controllability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>controllability</span></a> <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/control" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>control</span></a></p>
Cees Grootes<p>Why everyone should be at least a little bit worried about AI.</p><p>What have dozen of scientists, economists, researchers, and elected officials all have in common?</p><p>They are all worried about the near-term future of AI. The most worrisome thing of all? They are all worried about&nbsp;different&nbsp;things.</p><p><a href="https://garymarcus.substack.com/p/an-epic-ai-debateand-why-everyone" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">garymarcus.substack.com/p/an-e</span><span class="invisible">pic-ai-debateand-why-everyone</span></a></p><p><a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://mastodon.social/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ArtificialIntelligence</span></a> <a href="https://mastodon.social/tags/dangers" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dangers</span></a> <a href="https://mastodon.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://mastodon.social/tags/neuroscientis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscientis</span></a> <a href="https://mastodon.social/tags/social" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>social</span></a> <a href="https://mastodon.social/tags/socialresearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>socialresearch</span></a> <a href="https://mastodon.social/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>research</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/causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causality</span></a> <a href="https://mastodon.social/tags/GaryMarcus" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GaryMarcus</span></a> <span class="h-card"><a href="https://sigmoid.social/@garymarcus" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>garymarcus</span></a></span> <br><a href="https://mastodon.social/tags/sociology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sociology</span></a></p>
Cheng Soon Ong<p>If you are learning from a sequence of observations and actions, and using a probabilistic model, you should be treating the agent's actions as <a href="https://masto.ai/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> interventions. The authors attribute the lack of causal interventions as the reason for <a href="https://masto.ai/tags/delusions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>delusions</span></a>, and also propose a way to do <a href="https://masto.ai/tags/MetaLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MetaLearning</span></a>. <a href="https://masto.ai/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://masto.ai/tags/Causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Causality</span></a> <a href="https://masto.ai/tags/ReinforcementLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ReinforcementLearning</span></a></p><p><a href="https://arxiv.org/abs/2110.10819" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2110.10819</span><span class="invisible"></span></a></p>
Emre Kıcıman<p>Lots of new folks joining today so maybe worth reposting:</p><p>Hello <a href="https://hci.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> folks. I haven't run into a list of <a href="https://hci.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://hci.social/tags/causalinf" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causalinf</span></a> <a href="https://hci.social/tags/causality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causality</span></a> <a href="https://hci.social/tags/causalml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causalml</span></a> people on mastodon yet. I thought I'd start crowdsourcing.</p><p>Please share, edit, add yourself, etc. Thanks!</p><p><a href="https://docs.google.com/spreadsheets/d/1EzIBxQebN_ulvZfcasvoxlues03T_hcFCrnZPsVzmaw/edit?usp=sharing" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">docs.google.com/spreadsheets/d</span><span class="invisible">/1EzIBxQebN_ulvZfcasvoxlues03T_hcFCrnZPsVzmaw/edit?usp=sharing</span></a></p>
Emre Kıcıman<p>Tomorrow, Sonya Sharma, Swati Sharma and co-authors work on using <a href="https://hci.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> methods to improve our modeling of soil carbon process at the workshop on Climate Change and ML.</p><p><a href="https://www.microsoft.com/en-us/research/publication/causal-modeling-of-soil-processes-for-improved-generalization/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">microsoft.com/en-us/research/p</span><span class="invisible">ublication/causal-modeling-of-soil-processes-for-improved-generalization/</span></a></p>
Emre Kıcıman<p>And we also presented our work across MSR on open source <a href="https://hci.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> tools, incl. our python libraries for causal discovery and effect inference (Causica, EconML, DoWhy), plus our new no-code interfaces (ShowWhy).</p><p>ShowWhy demo vid: <a href="https://microsoft.com/en-us/research/video/introduction-to-showwhy-user-interfaces-for-causal-decision-making/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">microsoft.com/en-us/research/v</span><span class="invisible">ideo/introduction-to-showwhy-user-interfaces-for-causal-decision-making/</span></a></p><p>Paper "A Causal AI Suite for Decision-Making" <a href="https://www.microsoft.com/en-us/research/publication/a-causal-ai-suite-for-decision-making/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">microsoft.com/en-us/research/p</span><span class="invisible">ublication/a-causal-ai-suite-for-decision-making/</span></a></p>
Christoph<p>what ideas to improve <a href="https://fediscience.org/tags/Statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Statistics</span></a> would you present a group of <a href="https://fediscience.org/tags/psychology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>psychology</span></a> master students? <br>On my list so far are:</p><p>1. <a href="https://fediscience.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayesian</span></a> statistics (plus critique of frequentist)</p><p>2. use simulations to understand <a href="https://fediscience.org/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> generation processes + use it for power analyses (e.g. <a href="https://fediscience.org/tags/SuperPoweR" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SuperPoweR</span></a> from <span class="h-card"><a href="https://mastodon.social/@lakens" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>lakens</span></a></span> et al.)</p><p>3. think about whether you want to test a hypothesis or estimate an effect &amp; how to design experiments accordingly (inspired by <span class="h-card"><a href="https://mastodon.social/@EJWagenmakers" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>EJWagenmakers</span></a></span> )</p><p>4. maybe <a href="https://fediscience.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> <a href="https://fediscience.org/tags/analysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>analysis</span></a> </p><p><a href="https://fediscience.org/tags/OpenScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenScience</span></a> <a href="https://fediscience.org/tags/StatsTodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>StatsTodon</span></a></p>
Bertrand Charpentier<p><a href="https://sigmoid.social/tags/introduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>introduction</span></a> </p><p>I am a Ph.D. student in <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a>. My research interests cover <a href="https://sigmoid.social/tags/uncertainty" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>uncertainty</span></a> / <a href="https://sigmoid.social/tags/robustness" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>robustness</span></a> in machine learning, <a href="https://sigmoid.social/tags/hierarchical" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>hierarchical</span></a> / <a href="https://sigmoid.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> inference, and <a href="https://sigmoid.social/tags/efficient" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>efficient</span></a> machine learning :)</p>
Luisa N. Borrell<p><span class="h-card"><a href="https://med-mastodon.com/@gregggonsalves" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>gregggonsalves</span></a></span> Cannot agree more 💯💯Sometime a descriptive or unadjusted analysis is the way to go to determine a <a href="https://mastodon.online/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> effect.</p>
Esteban Moro<p><span class="h-card"><a href="https://sciences.social/@lecoursonnais" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>lecoursonnais</span></a></span> Nice to masto-meet you Maël! We also work on <a href="https://mastodon.social/tags/spatial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spatial</span></a> <a href="https://mastodon.social/tags/inequalities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inequalities</span></a> and <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> methods!!</p>
Maël Lecoursonnais<p><a href="https://sciences.social/tags/Introduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Introduction</span></a> </p><p>Hello Mastodon people ! I am a <a href="https://sciences.social/tags/PhD" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PhD</span></a> student in Analytical Sociology at Linköping University, Sweden.</p><p>I study <a href="https://sciences.social/tags/spatial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spatial</span></a> <a href="https://sciences.social/tags/inequalities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>inequalities</span></a>, including neighborhood effects and segregation. I use register data, <a href="https://sciences.social/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> methods, and <a href="https://sciences.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a>.</p><p>I also occasionally do R generative art and toot in English and French.</p>
Daniel Winkler<p>I'm not good at social media things, but here it is, an <a href="https://fosstodon.org/tags/introduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>introduction</span></a>: I'm a PhD student at the Vienna Uni of Economics and Business. I think about the <a href="https://fosstodon.org/tags/music" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>music</span></a> industry, <a href="https://fosstodon.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> stats and applied <a href="https://fosstodon.org/tags/econometrics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>econometrics</span></a> / <a href="https://fosstodon.org/tags/causal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causal</span></a> modelling mostly. I teach data-storytelling and marketing analytics. I'm also interested in <a href="https://fosstodon.org/tags/stoicism" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stoicism</span></a> and have two rabbits and a cat. @ me if you are interested in any of that :)</p>