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 #introduction time!
I’m from #SanFrancisco and moved back here after some wandering. Raising two kids and a dog. Working in tech (sigh) but on #sustainability at least.
Interested in and post about #CausalInference, #Statistics, #Politics, #Policy, #Climate, #Energy, #Dogs, #Crafting and #Parenting
Surveys, coincidences, statistical significance 
"What Educated Citizens Should Know About Statistics and Probability"
By Jessica Utts, in 2003: https://ics.uci.edu/~jutts/AmerStat2003.pdf via @hrefna
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 #causal #causalinference #DAGs #pharmacometrics, or clinical development #stats.
My old boss is hiring for Causal Inference work.
"Do you have some causal inference experience? Do you want a role that helps you build the science roadmap that impacts millions of customers every single day? I'm hiring in NYC and would love to talk to you."
#CausalInference #whypy #dowhy
https://www.amazon.jobs/en/jobs/2775999/senior-data-scientist-ring-data-science-and-engineering
Postdoc in dynamic causal gene network from single-cell & spatial multiomic
University of Massachusetts Chan Medical School
We seek #postdoc to decode dynamic #GeneRegulatoryNetwork from #SingleCell & #SpatialOmics with #CausalInference. Quantitative bg needed. Apply now!????
See the full job description on jobRxiv: https://...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-dynamic-causal-gene-network-from-single-cell-spatial-multiomic/?feed_id=83054
@joakinen 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 #causalinference: ask a #causal question. The overlap between (computer science) #engineering and #philosophy through #causality may be one of the clearest examples of this needed change of mindset [1]. @Jose_A_Alonso
[1] https://cs.ulb.ac.be/conferences/ebiss2023/slides/EBISS2023_slides_JordiVitria_1.IntroCausality.pdf
We also see how #DigitalTwins are increasingly used to make better decisions with digital twins of cities, spaces, traditional/vertical farms, supply chain graphs, and even the whole planet. This is where #Causalinference in DigitalTwins comes into play to bring direct and indirect monitoring to understanding the impact, through causal digital twins #CDT of new technologies, policy decisions, regulatory changes. This is especially important for #ClimateChange mitigation
#IoTday #IoTday2024 15/n
Looking forward to spending some time with this new #biorxiv #preprint "Causal inference with observational data and unobserved confounding variables," by Jarrett Byrnes and Laura Dee. It looks like an very useful work-through of practical issues in applying modern causal techniques to the kind of study design issues ecologists deal with.
This paper by @nickchk (https://doi.org/10.1080/1350178X.2022.2088085 ; ungated here: https: //ftp.cs.ucla.edu/pub/stat_ser/huntington-klein-jem-june2022.pdf) is the best, most accessible introduction and explanation of how DAGs can be useful for causal inference for people more familiar with potential outcomes and econometrics-style approaches #statsodon #CausalInference
![Title and abstract of the paper:
Pearl before economists: the book of why and empirical economics
Structural Causal Modeling (SCM) is an approach to causal inference closely associated with Judea Pearl and given an accessible instroduction in [Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books]. It is highly popular outside of economics, but has seen relatively little application within it. This paper briefly introduces the main concepts of SCM through the lens of whether applied economists are likely to find marginal benefit in these methods beyond standard economic approaches to causal inference. The most promising areas are those where SCM's causal diagrams alone offer significant value: covariate selection, the development of placebo tests, causal discovery, and identification in complex models.](https://fediscience.org/system/media_attachments/files/111/970/326/458/715/050/original/7e7470e773ca315f.png)
It's DAG day in class today and I *think* figured out a way to animatedly demonstrate collider bias (at least the selection bias version of it) #CausalInference #Statsodon
"Draw your assumptions before your conclusions."
5 years ago we launched the first version of the #CausalDiagrams course via HarvardX and edX.
This was the official trailer:
https://www.youtube.com/watch?v=SB2FxG-SdEQ
Since then, about 80,000 people in 180 countries have registered. The course is free for everyone in the world.
If you are interested in learning about Directed Acyclic Graphs (DAGs) and Single-World Intervention Graphs (SWIGs) for #causalinference, check it out:
https://www.edx.org/learn/data-analysis/harvard-university-causal-diagrams-draw-your-assumptions-before-your-conclusions
En lien avec ces réflexions concernant les statistiques observationnelles, voici un billet décrivant l'une de nos études récentes qui visait à appliquer les méthodes d'inférence causale à l' #audiologie : dans les pertes auditives liées à l'âge, peut-on séparer la contribution du vieillissement de l'oreille de celle des déficits cognitifs ? https://dbao.leo-varnet.fr/2021/05/06/age-et-pertes-dintelligibilite-chez-les-personnes-presbyacousiques-appareillees-retour-sur-le-projet-genopath/
#CausalInference #Statistics #Statistiques #Audiology #presbycusis #HearingLoss
I've thinking for some time about entering into causal inference, but I'm not sure how useful it can be for physics. I guess it's really helpful for social sciences and health sciences, but since in physics causality and causes are well established and known (at least in classical physics), I have doubts.
Are you people in #physics using #causalinference? #statistics
Added a new laptop sticker: @epiellie's "Eat Ice Cream; Do Crime" DAG showing the sun as confounder for ice cream and crime (get it and other causal inferencey things from https://www.redbubble.com/people/epiellie/shop) #CausalInference
My wonderful post-doc, health #economist Julia Dennett talks on her paper on the long-term effects of #OxyContin marketing in America. #harmreduction #causalinference #epidemiology #publichealth
https://www.healthaffairs.org/do/10.1377/hp20230731.712412/full/
Our new paper on the long-term impact of opioid manufacturers' targeted marketing to docs in states with high cancer burdens in the 1990s. 25 years later: more deaths from hepatitis and infective endocarditis. #opioids #Purdue #OcyContin #HCV #HBV #endocarditis #epidemiology #causalinference
https://www.healthaffairs.org/doi/10.1377/hlthaff.2023.00146
6/
In a more recent work [5], the authors tested LLMs on *pure* #causalInference tasks, where all variables are now symbolic (screenshot 1). They constructed systematically a dataset starting by picking variables, to generating all possible #causalGraphs, to finally mapping all possible statistical #correlations. They then “verbalize” these graphs into problems for LLMs to solve for a given causation hypothesis (screenshot 2).
I don't think I wrote a new #introduction when I migrated from .social.
I'm a #computational #InfectiousDisease #epidemiologist and #biostatistician. I work on #CausalInference and #NetworkScience methods in ID Epi and #DiseaseEcology #DisEcol
>66,000 individuals provided info at their doctor’s office or in their homes
4400 health professionals in >1400 primary care centers were mobilized
An information system for 2000 concurrent users was created
Agencies of the Spanish government and 17 regions were coordinated
No fancy #causalinference methods were used.
https://ajph.aphapublications.org/doi/10.2105/AJPH.2023.307233
Respect to colleagues who launched this study in less than 4 weeks in a health system under pandemic pressure.
Respect to descriptive studies.
