Mastodon

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #387 Give Your Hypotheses Space!Thoughts: "It’s tempting to throw a bunch of variables...into a model ...but proceed at your own caution!"<a href="https://mastodon.social/tags/Mbias" class="mention hashtag" rel="nofollow noopener" target="_blank">#Mbias</a> <a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#causalinference</a> <a href="https://mastodon.social/tags/collider" class="mention hashtag" rel="nofollow noopener" target="_blank">#collider</a> <a href="https://mastodon.social/tags/moderator" class="mention hashtag" rel="nofollow noopener" target="_blank">#moderator</a> <a href="https://mastodon.social/tags/confounder" class="mention hashtag" rel="nofollow noopener" target="_blank">#confounder</a> <a href="https://mastodon.social/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#regression</a> <a href="https://mastodon.social/tags/r" class="mention hashtag" rel="nofollow noopener" target="_blank">#r</a> <a href="https://mastodon.social/tags/DAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#DAG</a><a href="https://brian-lookabaugh.github.io/website-brianlookabaugh/blog/2025/mutual-adjustment/" rel="nofollow noopener" translate="no" target="_blank">https://brian-lookabaugh.github.io/website-brianlookabaugh/blog/2025/mutual-adjustment/</a>

Aneesh SatheBeyond the DatasetOn the recent season of the show Clarkson’s farm, J.C. goes through great lengths to buy the right pub. As with any sensible buyer, the team does a thorough tear down followed by a big build up before the place is open for business. They survey how the place is built, located, and accessed. In their refresh they ensure that each part of the pub is built with purpose. Even the tractor on the ceiling. The art is  in answering the question: How was this place put together? A data-scientist should be equally fussy. Until we trace how every number was collected, corrected and cleaned, —who measured it, what tool warped it, what assumptions skewed it—we can’t trust the next step in our business to flourish.<a href="https://aneeshsathe.com/wp-content/uploads/2025/07/image-from-rawpixel-id-3590280-jpeg.jpg" rel="nofollow noopener" target="_blank"></a>Old sound (1925) painting in high resolution by Paul Klee. Original from the Kunstmuseum Basel Museum. Digitally enhanced by rawpixel.Two load-bearing pillarsWhile there are many flavors of data science I’m concerned about the analysis that is done in scientific spheres and startups. In this world, the structure held up by two pillars:<ol><li>How we measure — the trip from reality to raw numbers. Feature extraction.</li><li>How we compare — the rules that let those numbers answer a question. Statistics and causality.</li></ol>Both of these related to having a deep understanding of the data generation process. Each from a different angle. A crack in either pillar and whatever sits on top crumbles. Plots, significance, AI predictions, mean nothing.How we measureA misaligned microscope is the digital equivalent of crooked lumber. No amount of massage can birth a photon that never hit the sensor. In fluorescence imaging, the point-spread function tells you how a pin-point of light smears across neighboring pixels; noise reminds you that light itself arrives from and is recorded by at least some randomness. Misjudge either and the cell you call “twice as bright” may be a mirage.In this data generation process the instrument nuances control what you see. Understanding this enables us to make judgements about what kind of post processing is right and which one may destroy or invent data. For simpler analysis the post processing can stop at cleaner raw data. For developing AI models, this process extends to labeling and analyzing data distributions. Andrew Ng’s approach, in data-centric AI, insists that tightening labels, fixing sensor drift, and writing clear provenance notes often beat fancier models.How we compareNow suppose Clarkson were to test a new fertilizer, fresh goat pellets, only on sunny plots. Any bumper harvest that follows says more about sunshine than about the pellets. Sound comparisons begin long before data arrive. A deep understanding of the science behind the experiment is critical before conducting any statistics. The wrong randomization, controls, and lurking confounder eat away at the foundation of statistics.This information is not in the data. Only understanding how the experiment was designed and which events preclude others enable us to build a model of the world of the experiment. Taking this lightly has large risks for startups with limited budgets and smaller experiments. A false positive result leads to wasted resources while a false negative presents opportunity costs.   The stakes climb quickly. Early in the COVID-19 pandemic, some regions bragged of lower death rates. Age, testing access, and hospital load varied wildly, yet headlines crowned local policies as miracle cures. When later studies re-leveled the footing, the miracles vanished. Why the pillars get skippedSpeed, habit, and misplaced trust. Leo Breiman warned in 2001 that many analysts chase algorithmic accuracy and skip the question of how the data were generated. What he called the “two cultures.” Today’s tooling tempts us even more: auto-charts, one-click models, pretrained everything. They save time—until they cost us the answer.The other issue is lack of a culture that communicates and shares a common language. Only in academic training is it possible to train a single person to understand the science, the instrumentation, and the statistics sufficiently that their research may be taken seriously. Even then we prefer peer review. There is no such scope in startups. Tasks and expertise must be split. It falls to the data scientist to ensure clarity and collecting information horizontally. It is the job of the leadership to enable this or accept dumb risks.Opening dayClarkson’s pub opening was a monumental task with a thousand details tracked and tackled by an army of experts. Follow the journey from phenomenon to file, guard the twin pillars of measure and compare, and reinforce them up with careful curation and open culture. Do that, and your analysis leaves room for the most important thing: inquiry.<a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/ai/" target="_blank">#AI</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/causal-inference/" target="_blank">#causalInference</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/clean-data/" target="_blank">#cleanData</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-centric-ai/" target="_blank">#dataCentricAI</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-provenance/" target="_blank">#dataProvenance</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-quality/" target="_blank">#dataQuality</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-science/" target="_blank">#dataScience</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/evidence-based-decision-making/" target="_blank">#evidenceBasedDecisionMaking</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/experiment-design/" target="_blank">#experimentDesign</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/feature-extraction/" target="_blank">#featureExtraction</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/foundation-engineering/" target="_blank">#foundationEngineering</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/instrumentation/" target="_blank">#instrumentation</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/measurement-error/" target="_blank">#measurementError</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/science/" target="_blank">#science</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/startup-analytics/" target="_blank">#startupAnalytics</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/statistical-analysis/" target="_blank">#statisticalAnalysis</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/statistics/" target="_blank">#statistics</a>

Carl Gold, PhDMy PR to the <a href="https://sigmoid.social/tags/EconML" class="mention hashtag" rel="nofollow noopener" target="_blank">#EconML</a> <a href="https://sigmoid.social/tags/PyWhy" class="mention hashtag" rel="nofollow noopener" target="_blank">#PyWhy</a> <a href="https://sigmoid.social/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#opensource</a> <a href="https://sigmoid.social/tags/causalai" class="mention hashtag" rel="nofollow noopener" target="_blank">#causalai</a> project was merged! 🎉 I made a small contribution by allowing a flexible choice of evaluation metric for scoring both the first stage and final stage models in Double Machine Learning (<a href="https://sigmoid.social/tags/DML" class="mention hashtag" rel="nofollow noopener" target="_blank">#DML</a>). Before, only the mean square error (MSE) was implemented. But as an ML practitioner "in the trenches" I have found that MSE is hard to interpret and compare across models. My new functions allow that 🙂 <a href="https://sigmoid.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a> <a href="https://sigmoid.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#machinelearning</a> <a href="https://sigmoid.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#datascience</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #383 Berkson's paradoxThoughts: aka Berkson's bias, collider bias, or Berkson's fallacy. Important for interpreting conditional probabilities. Can produce counterintuitive patterns.<a href="https://mastodon.social/tags/paradox" class="mention hashtag" rel="nofollow noopener" target="_blank">#paradox</a> <a href="https://mastodon.social/tags/collider" class="mention hashtag" rel="nofollow noopener" target="_blank">#collider</a> <a href="https://mastodon.social/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#bias</a> <a href="https://mastodon.social/tags/inference" class="mention hashtag" rel="nofollow noopener" target="_blank">#inference</a> <a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#causalinference</a><a href="https://en.m.wikipedia.org/wiki/Berkson's_paradox" rel="nofollow noopener" translate="no" target="_blank">https://en.m.wikipedia.org/wiki/Berkson's_paradox</a>

Tom StaffordSo far at this conference I have seen reports of true experiments, natural experiments, difference in difference analysis and regression discontinuity design - but no instrumental variable analysis I wonder why?I was hoping for the full set of causal inference methods<a href="https://mastodon.online/tags/ICSSI2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#ICSSI2025</a> <a href="https://mastodon.online/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #367 Matching in R: Propensity Scores, Weighting (IPTW) and the Double Robust EstimatorThoughts: A guide on common adjustments for observational studies.<a href="https://mastodon.social/tags/r" class="mention hashtag" rel="nofollow noopener" target="_blank">#r</a> <a href="https://mastodon.social/tags/observational" class="mention hashtag" rel="nofollow noopener" target="_blank">#observational</a> <a href="https://mastodon.social/tags/iptw" class="mention hashtag" rel="nofollow noopener" target="_blank">#iptw</a> <a href="https://mastodon.social/tags/matching" class="mention hashtag" rel="nofollow noopener" target="_blank">#matching</a> <a href="https://mastodon.social/tags/weights" class="mention hashtag" rel="nofollow noopener" target="_blank">#weights</a> <a href="https://mastodon.social/tags/doublerobust" class="mention hashtag" rel="nofollow noopener" target="_blank">#doublerobust</a> <a href="https://mastodon.social/tags/guide" class="mention hashtag" rel="nofollow noopener" target="_blank">#guide</a> <a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#causalinference</a><a href="https://www.franciscoyira.com/post/matching-in-r-3-propensity-score-iptw/" rel="nofollow noopener" translate="no" target="_blank">https://www.franciscoyira.com/post/matching-in-r-3-propensity-score-iptw/</a>

MinmiTheDinoWhat are people’s fave methods for this situation:At t0, all units are untreated. As time goes on, individual units are one by one selected for treatment, on an expert’s assessment of their potential improvement under treatment. How to measure the treatment effect, either over all units or ideally the treatment effect on each unit?Oh, for extra fun, they’re probably not independent<a href="https://sfba.social/tags/Statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Statistics</a> <a href="https://sfba.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a> <a href="https://sfba.social/tags/Econometrics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Econometrics</a>

Christian RöverRegistration is open for the GMDS ACADEMY 2025 (Hannover, October 20-23). There will be three parallel workshops on meta analysis, causal inference and time-to-event analysis involving Wolfgang Viechtbauer (<a href="https://scholar.social/@wviechtb" class="u-url mention" rel="nofollow noopener" target="_blank">@wviechtb</a>), Christian Röver, Sebastian Weber, Vanessa Didelez, Arthur Allignol, Oliver Kuß, Alexandra Strobel, Hannes Buchner, Xiaofei Liu and Ann-Kathrin Ozga. See here for more details: 👉 <a href="https://www.gmds.de/fileadmin/user_upload/GMDS-Academy-2025.pdf" rel="nofollow noopener" translate="no" target="_blank">https://www.gmds.de/fileadmin/user_upload/GMDS-Academy-2025.pdf</a><a href="https://mastodon.social/tags/MetaAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#MetaAnalysis</a> <a href="https://mastodon.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a> <a href="https://mastodon.social/tags/SurvivalAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#SurvivalAnalysis</a> <a href="https://mastodon.social/tags/GMDS" class="mention hashtag" rel="nofollow noopener" target="_blank">#GMDS</a>

LLMsUnlocking Generative AI: A Deep Dive into Causal Language Models The landscape of artificial inte... <a rel="nofollow noopener" class="mention hashtag" href="https://mastodon.social/tags/llm" target="_blank">#llm</a> <a rel="nofollow noopener" class="mention hashtag" href="https://mastodon.social/tags/ai" target="_blank">#ai</a> <a rel="nofollow noopener" class="mention hashtag" href="https://mastodon.social/tags/causal-inference" target="_blank">#causal-inference</a> <a rel="nofollow noopener" class="mention hashtag" href="https://mastodon.social/tags/machine-learning" target="_blank">#machine-learning</a> <a rel="nofollow noopener" class="mention hashtag" href="https://mastodon.social/tags/nlp" target="_blank">#nlp</a> <a href="https://medium.com/@danaasa/unlocking-generative-ai-a-deep-dive-into-causal-language-models-1c96fe1e6b6d?source=rss------machine_learning-5" rel="nofollow noopener" target="_blank">Origin</a> | <a href="https://awakari.com/sub-details.html?id=LLMs" rel="nofollow noopener" target="_blank">Interest</a> | <a href="https://awakari.com/pub-msg.html?id=94k53rOrgVZYnp9TPuG16UIpnQe&interestId=LLMs" rel="nofollow noopener" target="_blank">Match</a>

मेंथीCausal inference feels like pretty much the most important topic one can think of in statistics or even for humanity in general. So why is the entire field dominated by just one or two people (obviously I'm referring to Judea Pearl and/or Donald Rubin)? It feels rather... cultish. Can any folks in the field opine why it is so dominated by one or two individuals, compared to any other important area of research today?<a href="https://social.seattle.wa.us/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a> <a href="https://social.seattle.wa.us/tags/Statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Statistics</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #348 The Effect {book} - Causal DiagramsThoughts: At some point you'll need to learn about DAGs. Maybe this is the chapter you need.<a href="https://mastodon.social/tags/DAGs" class="mention hashtag" rel="nofollow noopener" target="_blank">#DAGs</a> <a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#causalinference</a> <a href="https://mastodon.social/tags/guide" class="mention hashtag" rel="nofollow noopener" target="_blank">#guide</a> <a href="https://mastodon.social/tags/book" class="mention hashtag" rel="nofollow noopener" target="_blank">#book</a> <a href="https://mastodon.social/tags/education" class="mention hashtag" rel="nofollow noopener" target="_blank">#education</a> <a href="https://mastodon.social/tags/ebook" class="mention hashtag" rel="nofollow noopener" target="_blank">#ebook</a><a href="https://theeffectbook.net/ch-CausalDiagrams.html" rel="nofollow noopener" translate="no" target="_blank">https://theeffectbook.net/ch-CausalDiagrams.html</a>

jobRxivPostdoc in Single-Cell Multi-Omic Gene Regulatory Networks University of Massachusetts Chan Medical SchoolJoin us to decode <a href="https://mas.to/tags/GeneRegulatoryNetwork" class="mention hashtag" rel="nofollow noopener" target="_blank">#GeneRegulatoryNetwork</a> from <a href="https://mas.to/tags/SingleCell" class="mention hashtag" rel="nofollow noopener" target="_blank">#SingleCell</a> multiomics with <a href="https://mas.to/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a> as a <a href="https://mas.to/tags/postdoc" class="mention hashtag" rel="nofollow noopener" target="_blank">#postdoc</a>! Quantitative bg needed.See the full job description on jobRxiv: <a href="https://jobrxiv.org/job/university-of-massachusetts-chan-medica" rel="nofollow noopener" translate="no" target="_blank">https://jobrxiv.org/job/university-of-massachusetts-chan-medica</a>... <a href="https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=94702" rel="nofollow noopener" translate="no" target="_blank">https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=94702</a>

MinmiTheDinoHello 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" target="_blank">#introduction</a> time!I’m from <a href="https://sfba.social/tags/SanFrancisco" class="mention hashtag" rel="nofollow noopener" target="_blank">#SanFrancisco</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" target="_blank">#sustainability</a> at least. Interested in and post about <a href="https://sfba.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a>, <a href="https://sfba.social/tags/Statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Statistics</a>, <a href="https://sfba.social/tags/Politics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Politics</a>, <a href="https://sfba.social/tags/Policy" class="mention hashtag" rel="nofollow noopener" target="_blank">#Policy</a>, <a href="https://sfba.social/tags/Climate" class="mention hashtag" rel="nofollow noopener" target="_blank">#Climate</a>, <a href="https://sfba.social/tags/Energy" class="mention hashtag" rel="nofollow noopener" target="_blank">#Energy</a>, <a href="https://sfba.social/tags/Dogs" class="mention hashtag" rel="nofollow noopener" target="_blank">#Dogs</a>, <a href="https://sfba.social/tags/Crafting" class="mention hashtag" rel="nofollow noopener" target="_blank">#Crafting</a> and <a href="https://sfba.social/tags/Parenting" class="mention hashtag" rel="nofollow noopener" target="_blank">#Parenting</a>

jobRxivPostdoc in Single-Cell Multi-Omic Gene Regulatory Networks University of Massachusetts Chan Medical SchoolJoin us to decode <a href="https://mas.to/tags/GeneRegulatoryNetwork" class="mention hashtag" rel="nofollow noopener" target="_blank">#GeneRegulatoryNetwork</a> from <a href="https://mas.to/tags/SingleCell" class="mention hashtag" rel="nofollow noopener" target="_blank">#SingleCell</a> multiomics with <a href="https://mas.to/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a> as a <a href="https://mas.to/tags/postdoc" class="mention hashtag" rel="nofollow noopener" target="_blank">#postdoc</a>! Quantitative bg needed.See the full job description on jobRxiv: <a href="https://jobrxiv.org/job/university-of-massachusetts-chan-medica" rel="nofollow noopener" translate="no" target="_blank">https://jobrxiv.org/job/university-of-massachusetts-chan-medica</a>... <a href="https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=94125" rel="nofollow noopener" translate="no" target="_blank">https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=94125</a>

Martin ModrákThis looks great: Andrew Gelman (<a href="https://bayes.club/@statmodeling_bot" class="u-url mention" rel="nofollow noopener" target="_blank">@statmodeling_bot</a> ) would be joining Nancy Cartwright and Berna Devezer. Short idea talks, lots of panel discussion and Q&A. Join us on April 25th to discuss RCTs, replications, and scientific inference. <a href="https://sites.google.com/view/cepbi/talks-gatherings?authuser=0" rel="nofollow noopener" translate="no" target="_blank">https://sites.google.com/view/cepbi/talks-gatherings?authuser=0</a><a href="https://bayes.club/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#stats</a> <a href="https://bayes.club/tags/causalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#causalInference</a> <a href="https://bayes.club/tags/RCTs" class="mention hashtag" rel="nofollow noopener" target="_blank">#RCTs</a> <a href="https://bayes.club/tags/philsci" class="mention hashtag" rel="nofollow noopener" target="_blank">#philsci</a>

Ingo RohlfingThe 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" translate="no" target="_blank">https://doi.org/10.1017/psrm.2025.17</a> <a href="https://mastodon.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</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/

jobRxivPostdoc in Single-Cell Multi-Omic Gene Regulatory Networks University of Massachusetts Chan Medical SchoolJoin us to decode <a href="https://mas.to/tags/GeneRegulatoryNetwork" class="mention hashtag" rel="nofollow noopener" target="_blank">#GeneRegulatoryNetwork</a> from <a href="https://mas.to/tags/SingleCell" class="mention hashtag" rel="nofollow noopener" target="_blank">#SingleCell</a> multiomics with <a href="https://mas.to/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a> as a <a href="https://mas.to/tags/postdoc" class="mention hashtag" rel="nofollow noopener" target="_blank">#postdoc</a>! Quantitative bg needed.See the full job description on jobRxiv: <a href="https://jobrxiv.org/job/university-of-massachusetts-chan-medica" rel="nofollow noopener" translate="no" target="_blank">https://jobrxiv.org/job/university-of-massachusetts-chan-medica</a>... <a href="https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=93671" rel="nofollow noopener" translate="no" target="_blank">https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=93671</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #307 The C-word, the P-word, and realism in epidemiologyThoughts: A comment on #306. Causal inference in observational research is a confusing matter. Read both.<a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#causalinference</a> <a href="https://mastodon.social/tags/observational" class="mention hashtag" rel="nofollow noopener" target="_blank">#observational</a> <a href="https://mastodon.social/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#research</a> <a href="https://mastodon.social/tags/commentary" class="mention hashtag" rel="nofollow noopener" target="_blank">#commentary</a><a href="https://link.springer.com/article/10.1007/s11229-019-02169-x" rel="nofollow noopener" translate="no" target="_blank">https://link.springer.com/article/10.1007/s11229-019-02169-x</a>

Dr Mircea Zloteanu ☀️ 🌊🌴<a href="https://mastodon.social/tags/statstab" class="mention hashtag" rel="nofollow noopener" target="_blank">#statstab</a> #306 The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational DataThoughts: Causal inference is messy business. Maybe we need to be more honest about that.<a href="https://mastodon.social/tags/causalinference" class="mention hashtag" rel="nofollow noopener" target="_blank">#causalinference</a> <a href="https://mastodon.social/tags/observational" class="mention hashtag" rel="nofollow noopener" target="_blank">#observational</a> <a href="https://mastodon.social/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#research</a> <a href="https://mastodon.social/tags/confounds" class="mention hashtag" rel="nofollow noopener" target="_blank">#confounds</a><a href="https://doi.org/10.2105/AJPH.2018.304337" rel="nofollow noopener" translate="no" target="_blank">https://doi.org/10.2105/AJPH.2018.304337</a>

jobRxivPostdoc in Single-Cell Multi-Omic Gene Regulatory Networks University of Massachusetts Chan Medical SchoolJoin us to decode <a href="https://mas.to/tags/GeneRegulatoryNetwork" class="mention hashtag" rel="nofollow noopener" target="_blank">#GeneRegulatoryNetwork</a> from <a href="https://mas.to/tags/SingleCell" class="mention hashtag" rel="nofollow noopener" target="_blank">#SingleCell</a> multiomics with <a href="https://mas.to/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#CausalInference</a> as a <a href="https://mas.to/tags/postdoc" class="mention hashtag" rel="nofollow noopener" target="_blank">#postdoc</a>! Quantitative bg needed.See the full job description on jobRxiv: <a href="https://jobrxiv.org/job/university-of-massachusetts-chan-medica" rel="nofollow noopener" translate="no" target="_blank">https://jobrxiv.org/job/university-of-massachusetts-chan-medica</a>... <a href="https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=93482" rel="nofollow noopener" translate="no" target="_blank">https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=93482</a>

Recent searches

Search options

Administered by:

Server stats:

#causalinference