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#causalinference

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MinmiTheDino<p>Hello SFBA! I’ve been wistfully thinking of switching over here for a while and recent fosstodon choices gave me the push I needed. So <a href="https://sfba.social/tags/introduction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>introduction</span></a> time!</p><p>I’m from <a href="https://sfba.social/tags/SanFrancisco" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SanFrancisco</span></a> and moved back here after some wandering. Raising two kids and a dog. Working in tech (sigh) but on <a href="https://sfba.social/tags/sustainability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sustainability</span></a> at least. </p><p>Interested in and post about <a href="https://sfba.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CausalInference</span></a>, <a href="https://sfba.social/tags/Statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Statistics</span></a>, <a href="https://sfba.social/tags/Politics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Politics</span></a>, <a href="https://sfba.social/tags/Policy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Policy</span></a>, <a href="https://sfba.social/tags/Climate" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Climate</span></a>, <a href="https://sfba.social/tags/Energy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Energy</span></a>, <a href="https://sfba.social/tags/Dogs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Dogs</span></a>, <a href="https://sfba.social/tags/Crafting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Crafting</span></a> and <a href="https://sfba.social/tags/Parenting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Parenting</span></a></p>
Martin Modrák<p>This looks great: Andrew Gelman (<span class="h-card" translate="no"><a href="https://bayes.club/@statmodeling_bot" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>statmodeling_bot</span></a></span> ) would be joining Nancy Cartwright and Berna Devezer. Short idea talks, lots of panel discussion and Q&amp;A. </p><p>Join us on April 25th to discuss RCTs, replications, and scientific inference. <br><a href="https://sites.google.com/view/cepbi/talks-gatherings?authuser=0" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">sites.google.com/view/cepbi/ta</span><span class="invisible">lks-gatherings?authuser=0</span></a></p><p><a href="https://bayes.club/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://bayes.club/tags/causalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causalInference</span></a> <a href="https://bayes.club/tags/RCTs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RCTs</span></a> <a href="https://bayes.club/tags/philsci" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>philsci</span></a></p>
Ingo Rohlfing<p>The case for multiple UESDs and an application to migrant deaths in the Mediterranean Sea <a href="https://doi.org/10.1017/psrm.2025.17" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.1017/psrm.2025.17</span><span class="invisible"></span></a> <a href="https://mastodon.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CausalInference</span></a> Analyzing multiple, comparable unexpected events happening during survey data collection makes a lot of sense to assess patterns. In doing so, one has to follow 1/</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> #307 The C-word, the P-word, and realism in epidemiology</p><p>Thoughts: A comment on #306. Causal inference in observational research is a confusing matter. Read both.</p><p><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/observational" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>observational</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/commentary" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>commentary</span></a></p><p><a href="https://link.springer.com/article/10.1007/s11229-019-02169-x" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">link.springer.com/article/10.1</span><span class="invisible">007/s11229-019-02169-x</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> #306 The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data</p><p>Thoughts: Causal inference is messy business. Maybe we need to be more honest about that.</p><p><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/observational" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>observational</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/confounds" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>confounds</span></a></p><p><a href="https://doi.org/10.2105/AJPH.2018.304337" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.2105/AJPH.2018.3043</span><span class="invisible">37</span></a></p>
Dr. LabRat<p><span class="h-card" translate="no"><a href="https://framapiaf.org/@newsycombinator" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>newsycombinator</span></a></span> An illustrative example of collider bias: Location -&gt; Restaurant Success &lt;- Food Quality</p><p>I recall that <span class="h-card" translate="no"><a href="https://nerdculture.de/@rlmcelreath" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>rlmcelreath</span></a></span> used it in one of his lectures… It is surprising how many students of <a href="https://fediscience.org/tags/causalinference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causalinference</span></a> (mostly the ones that overtly reject DAGs as a useful source of knowledge representation) miss this effect. I vividly recall how Donald Rubin himself told me that he had never seen any colliders in the real world while answering questions after a lecture at Northwestern. ¯\_(ツ)_/¯</p>
LavX News<p>Unveiling ModePlait: A Breakthrough in Time-Evolving Causality Modeling for Data Streams</p><p>In a world increasingly driven by data, understanding the causal relationships that evolve over time is crucial. ModePlait, a novel model developed by Chihara et al., promises to redefine how we analy...</p><p><a href="https://news.lavx.hu/article/unveiling-modeplait-a-breakthrough-in-time-evolving-causality-modeling-for-data-streams" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="ellipsis">news.lavx.hu/article/unveiling</span><span class="invisible">-modeplait-a-breakthrough-in-time-evolving-causality-modeling-for-data-streams</span></a></p><p><a href="https://mastodon.cloud/tags/news" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>news</span></a> <a href="https://mastodon.cloud/tags/tech" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tech</span></a> <a href="https://mastodon.cloud/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> <a href="https://mastodon.cloud/tags/CausalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CausalInference</span></a> <a href="https://mastodon.cloud/tags/TimeSeriesForecasting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TimeSeriesForecasting</span></a></p>
Tom Stafford<p>Covid-19 Pandemic as a Natural Experiment: The Case of Home Advantage in Sports </p><p><a href="https://journals.sagepub.com/doi/10.1177/09637214241301300" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">journals.sagepub.com/doi/10.11</span><span class="invisible">77/09637214241301300</span></a> </p><p>"The COVID-19 pandemic, with its unparalleled disruptions, offers a unique opportunity to isolate causal effects and test previously impossible hypotheses. Here, we examine the home advantage (HA) in sports—a phenomenon in which teams generally perform better in front of their home fans—and how the pandemic-induced absence of fans offered... natural experiment. "</p><p><a href="https://mastodon.online/tags/CausalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CausalInference</span></a></p>
Dennis Alexis Valin Dittrich<p>via <span class="h-card" translate="no"><a href="https://fediscience.org/@MiguelHernan" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>MiguelHernan</span></a></span>:<br>Upgrade your <a href="https://fediscience.org/tags/causalinference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causalinference</span></a> arsenal. A revision of our book "Causal Inference: What If" is available at <a href="https://miguelhernan.org/whatifbook" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">miguelhernan.org/whatifbook</span><span class="invisible"></span></a> Thanks to everyone who suggested improvements, reported typos, and proposed new citations and material. Enjoy the <a href="https://fediscience.org/tags/WhatIfBook" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>WhatIfBook</span></a> plus code and data. Also, it's free.”<br><a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>rstats</span></a></p>
Ingo Rohlfing<p>Are lightning strikes the new rainfall IV?<br><a href="https://blogs.worldbank.org/en/impactevaluations/are-lightning-strikes-the-new-rainfall-iv-" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">blogs.worldbank.org/en/impacte</span><span class="invisible">valuations/are-lightning-strikes-the-new-rainfall-iv-</span></a><br><a href="https://mastodon.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CausalInference</span></a> Isn't every instrument overused at some point? "overused" meaning too many studies use it for identifying all kinds of different relationships, making it implausible that exclusion restriction always holds <a href="https://mastodon.social/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a></p>
renebekkers<p>Causalingofication - describing an association as if it is causal, without a research design allowing for causal inference.<br><a href="https://detectingbadscience.wordpress.com/2024/12/16/causalingofication/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">detectingbadscience.wordpress.</span><span class="invisible">com/2024/12/16/causalingofication/</span></a><br>Correlation is not causation. Yet research reports often make causal claims, even when the research design does not allow for them. Researchers regularly describe the results of a regression as if the correlates have “effects” on the outcome. <br><a href="https://mastodon.social/tags/badscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>badscience</span></a> <a href="https://mastodon.social/tags/betterscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>betterscience</span></a> <a href="https://mastodon.social/tags/causality" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causality</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/credibility" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>credibility</span></a> <a href="https://mastodon.social/tags/experiment" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>experiment</span></a> <a href="https://mastodon.social/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</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> #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>
Eric Maugendre<p>Surveys, coincidences, statistical significance 🧵</p><p>"What Educated Citizens Should Know About Statistics and Probability"<br>By Jessica Utts, in 2003: <a href="https://ics.uci.edu/~jutts/AmerStat2003.pdf" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">ics.uci.edu/~jutts/AmerStat200</span><span class="invisible">3.pdf</span></a> via <span class="h-card" translate="no"><a href="https://hachyderm.io/@hrefna" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>hrefna</span></a></span> </p><p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/edutooters" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>edutooters</span></a></span></p><p><a href="https://hachyderm.io/tags/nullHypothesis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>nullHypothesis</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/pValues" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pValues</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/education" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>education</span></a> <a href="https://hachyderm.io/tags/higherEd" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>higherEd</span></a> <a href="https://hachyderm.io/tags/statisticalLiteracy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statisticalLiteracy</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/media" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>media</span></a> <a href="https://hachyderm.io/tags/causalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causalInference</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>
Ingo Rohlfing<p>A graphical method for causal program attribution in theory-based evaluation<br><a href="https://journals.sagepub.com/doi/full/10.1177/13563890231223171" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">journals.sagepub.com/doi/full/</span><span class="invisible">10.1177/13563890231223171</span></a> <br>A useful, applied introduction to graphical causal models in evaluation research. From what I've read, I'd take minor issues with the argument that this is also helpful 1/<br><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/evaluation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>evaluation</span></a> <a href="https://mastodon.social/tags/theory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>theory</span></a></p>
Ingo Rohlfing<p>Estimating Treatment Effects Using the Front‐Door Criterion<br><a href="https://onlinelibrary.wiley.com/doi/full/10.1111/obes.12598" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">onlinelibrary.wiley.com/doi/fu</span><span class="invisible">ll/10.1111/obes.12598</span></a> <a href="https://mastodon.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CausalInference</span></a> Nice illustration of front-door criterion that is probably much more often discussed in causal inference classes than used in practice. Maybe also because articles like this one have been rare, as far as I know <a href="https://mastodon.social/tags/Statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Statistics</span></a></p>
Ingo Rohlfing<p>New from the "correlation is not causation" category: GDP/capita and # of pinball machines/capita <a href="https://www.sumsar.net/blog/pinball-machines-per-capita/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">sumsar.net/blog/pinball-machin</span><span class="invisible">es-per-capita/</span></a> Or is it correlation and causation? Not implausible that # of machines is at the end of a causal chain that starts with economic wealth <a href="https://mastodon.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CausalInference</span></a></p>
Felix Schönbrodt<p>One of my favorite science blogs, the 100% CI, has published a new blog post: „Sometimes a <a href="https://scicomm.xyz/tags/causaleffect" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causaleffect</span></a> is just a causal effect (regardless of how it’s mediated or moderated)“ (by Julia Rohrer).</p><p>I like the opener: "It’s probably fair to say that many psychological researchers are somewhat confused about causal inference.“</p><p><a href="https://scicomm.xyz/tags/CausalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CausalInference</span></a></p>
Ingo Rohlfing<p>Causal Inference in R<br><a href="https://www.r-causal.org/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">r-causal.org/</span><span class="invisible"></span></a><br>The book is in the making. What is available so far looks useful for teaching and accessibly written <a href="https://mastodon.social/tags/rstats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>rstats</span></a> <a href="https://mastodon.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CausalInference</span></a></p>
Ingo Rohlfing<p>On the uses and abuses of regression models: a call for reform of statistical practice and teaching<br><a href="https://arxiv.org/abs/2309.06668" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2309.06668</span><span class="invisible"></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/Statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Statistics</span></a> <br>The distinction btw description, prediction and causal analysis makes a lot of science. I see that one can use bivariate regression for 1/</p>