Andrei

Cover of The Possible Form of an Interlocution: W. E. B. Du Bois and Max Weber in Correspondence by Nahum Dimitri Chandler. It features a black-and-white photograph of W.E.B. Du Bois wearing a dark suit, vest, white shirt, and bow tie, set against a gray background. The left side of the cover features a bold vertical red stripe. Below the photograph, a dark brown section contains the title in beige text. The author's name is printed at the bottom in the same beige font.

Nahum Dimitri Chandler analyzes correspondence between W. E. B. Du Bois and Max Weber in "The Possible Form of an Interlocution." Read the intro for free now. #BlackStudies #SocialTheory buff.ly/Uh89Quh

En train de regarder la première saison de Foundation (très bien) et: ils en font des caisses avec les maths et la psycho alors que, factuellement, le gars est SOCIOLOGUE. Mais sûrement que mathématicien, ça devait faire plus crédible.

Why GeoParquet Is A Poor Cloud Native Format · cloudnativegeo cloud-optimized-geospatial-formats-guide · Discussion #82 (Geo)Parquet is an extremely useful column oriented data format. When working with local, massive data sets having many millions of features, the performance of this format is second to none. Plent...

Found a rich seam here: github.com/cloudnativeg...

WE ARE HIRING! 2 Lecturers in Quantitative Social Science. Want a friendly interdisciplinary department in one of the world's most vibrant cities? This just might be for you.

Apply by: 10 Oct

www.ucl.ac.uk/work-at-ucl/...

link 📈🤖
Causal Inference for Circular Data (Wu) In causal inference, a fundamental task is to estimate the effect resulting from a specific treatment, which is often handled with inverse probability weighting. Despite an abundance of attention to the advancement of this task, most articles have f

Tatsuru Kikuchi: A Unified Framework for Spatial and Temporal Treatment Effect Boundaries: Theory and Identification https://arxiv.org/abs/2510.00754 https://arxiv.org/pdf/2510.00754 https://arxiv.org/html/2510.00754

Elucidating some common biases in randomized controlled trials using directed acyclic graphs Although the ideal randomized clinical trial is the gold standard for causal inference, real randomized trials often suffer from imperfections that may hamper causal effect estimation. Stating the estimand of interest can help reduce confusion about what is being estimated, but it is often difficult to determine what is and is not identifiable given a trial’s specific imperfections. We demonstrate how directed acyclic graphs can be used to elucidate the consequences of common imperfections, such as noncompliance, unblinding, and drop-out, for the identification of the intention-to-treat effect, the total treatment effect and the physiological treatment effect. We assert that the physiological treatment effect is not identifiable outside a trial with perfect compliance and no dropout, where blinding is perfectly maintained

Table 1 showing the Identifiability of target estimands depending on whether there is blinding, full compliance, and no drop-out

An example DAG from the paper. Fig. 4: A blinded trial with noncompliance. U are unobserved confounders, Z is treatment assignment, C is compliance, X is the realized treatment, S is the subject's physical and mental health status, Xself and Xcln are the treatment that the participant and the clinician believed the participant received, Y is the outcome.

Just finished reading this *excellent* article by Gabriel et al. which discusses which effects can be identified in randomized controlled trials. With DAGs!>

link.springer.com/article/10.1...

TOMORROW 12 NOON Paris time: CREST Sociology seminar by @sungju.bsky.social: "The Paradox of Place: How Emotional Connections Shape Community Responses to Flood Risks"

In person at ENSAE or on-line on Zoom!

Urbanists OUTSIDE North America Worth Following! Join the conversation

NEW STARTER PACK! This time I’m hoping to encourage and support global urbanists from OUTSIDE North America here on Bluesky, so hopefully this will help! Who’ve I missed? Just joined? Let me know! I’ll keep updating, so please keep checking & sharing this pack! And let’s try using #GlobalUrbanists.

Introducing SedonaDB: A single-node analytical database engine with geospatial as a first-class citizen

The Apache Sedona community is excited to announce the initial release of SedonaDB! 🎉 SedonaDB is the first open-source, single-node analytical database engine that treats spatial data as a…

A skull unearthed in China challenges the timeline of human evolution, scientists say A badly crushed cranium unearthed decades ago from a riverbank in central China that once defied classification is now shaking up the human family tree, according to a new analysis.

A skull unearthed in China challenges the timeline of human evolution, scientists say

www.ctvnews.ca/sci-tech/art...

#141 AI Assisted Causal Inference, with Sam Witty Quickly and easily listen to Learning Bayesian Statistics for free!

player.captivate.fm/episode/6376...

Glob or not to blob, that is the question...

link 📈🤖
Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference (Cinelli, Feller, Imbens et al) Causality and causal inference have emerged as core research areas at the interface of modern statistics and domains including biomedical sciences, social sciences, computer scie

So You Want to Be an Academic? What I Wish I Knew Early in Graduate School Blog for junior PhD students on work, visibility, community, and sanity—long before the faculty job market is on the horizon.

A must read...

Job Opportunity: Membership, Programmes & Events Assistant | The Political Studies Association (PSA) Job Opportunity: PSA Membership, Programmes & Events AssistantThe Political Studies Association (PSA) is seeking an enthusiastic and detail-oriented individual to join our team as Membership,…

*PSA JOB OPPORTUNITY* 📢 Join our team as PSA #Membership, Programmes & #Events Assistant (PT 0.6,Fixed term) to support with new projects and Annual Conference. Open to all including #PhdStudents and #EarlyCareerResearchers
📆 Application deadline Friday 3 October
➡️

Do not use nature like it's for free. It's the same for new buildings which should be rather on top of existing buildings in many cases. QUICK CHECK of your building here: www.optoppen.org

Lack of geographic diversity in peer review skews “publishable” research - Impact of Social Sciences An analysis of peer review data for 204,718 submissions to 60 Institute of Physics journals finds that reviewers tend to favour submissions from their own countries.

Analysing submissions to 60 Institute of Physics journals, @jamesmzd.bsky.social & Misha Teplitskiy find that #PeerReviewers tend to favour submissions from their own countries, meaning authors from countries with few reviewers face a major structural barrier.

@lseimpactblog.bsky.social

In my reading list, best.

New preprint out now: how do #neighborhood #networks and local work norms affect the integration of #refugee women? With geodata, @sebbaehr.bsky.social and I show that especially employed female neighbors seem to play a decisive role ♀️🏠
osf.io/preprints/so...

graph

When US-born Americans move, do they go toward or away from immigrants? When they move between states, it's toward places with more immigrants, especially when young people move. Within-state moves over age 30, destinations have smaller immigrant shares. (If I did this right)

Milton Keynes residents share mixed opinions on new estates Researchers interview people living on three new-build estates in Milton Keynes.

The 'unnoticed and unseen' struggles on new estates

#Bedfordshire #BedsBucksHerts

🔗: https://www.bbc.com/news/arti...

THIS THURSDAY 12 NOON Paris time: Ashley Mears at the CREST Sociology seminar, talking about "Learning to Like the Likes and the Hate: The Labor of Internet Fame in the New Attention Economy"

In person at ENSAE and on-line: zoom.us/j/9730867636...

Come and join us!

"People can see status without seeking status" Per Engzell. Research into social stratification presents a puzzle. On the one hand, across social strata, people agree on a hierarchy of prestige between occupations. On the other, they differ widely in the extent to which they seek social advancement for themselves or their children. If occupational values were shared, we would expect differences in status seeking to be smaller. I resolve this puzzle by distinguishing between consensus beliefs--what we think 'most others value--and what we ourselves value. From this, I sketch a model of status alignment showing how differing preferences shape taste for status versus other attributes. I contrast this with dominant theories that attribute social reproduction to status maintenance: the desire to avoid downward social mobility. A novel empirical test using Swedish survey data shows that status alignment offers the most consistent explanation and best model fit when mothers are asked to evaluate 82 occupations for their child. Across social strata, mothers hold accurate beliefs about what others want, but differ in what they want for their child. Status alignment, not status maintenance, drives social reproduction.

Happy to be in Amsterdam today to present some work that bites the hand that feeds me

An LLM that never hallucinates There are two big errors we can make with information, but one often gets forgotten

Want a large language model that never hallucinates? Easy: just get it to always print “I don’t know”, regardless of the input.

If you find this solution is a bit unsatisfying, you’re not alone. But it hits on a crucial trade-off with learning machines and decision-making more generally.

New post:

Slide titled "Types of validity" Violence in video games affects aggression in youths. A affects B in C. This statement is a generalization, of which any part could potentially be invalid. Formalization of types of validity according to Estlinger et al., 2025

Slide titled: Overview validity A influences B in C Internal Validity: A influences B, rather than just being correlated with it. Construct validity (of the manipulation/treatment/cause): It is indeed A that has the effect, and not something else. Construct validity (of the effect, the outcome): It is indeed B that is being influenced External validity: The effect indeed occurs in C.

Getting ready to teach internal/external/construct validity, for the first time using the framing by Estlinger, @davebrady72.bsky.social and @eschwitz.bsky.social (www.degruyterbrill.com/document/doi...).

Super happy because this is much clearer than what I told student before!

Walkable Neighborhoods Help Adults Socialize, Increase Community Adults who live in walkable neighborhoods are more likely to socialize and have a stronger sense of community, report researchers at the UC San Diego Herbert Wertheim School of Public Health and Human...

“Loneliness and isolation can lead to: 29% increased risk of heart disease; 32% increased risk of stroke; 50% increased risk of developing dementia among older adults; and 60% increased risk of premature death.”

Surgeon General recommends designing cities to create more connections. #WalkableCities

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...

📢 Join our next Active Bystander Training for economists!

🗓️ 18 Nov 2025 | 09:30–11:00 | Online
Learn tools for safe, effective intervention & fostering an inclusive profession.

👉 Register: ProgrammesOfficer@res.org.uk
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#Economics #EDI #EconSky

Arthur Lewbel, Xi Qu, Xun Tang: Estimating Social Network Models with Link Misclassification https://arxiv.org/abs/2509.07343 https://arxiv.org/pdf/2509.07343 https://arxiv.org/html/2509.07343