iiinova — where your digital self keeps evolving

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Paul Hünermund @p-hunermund.com · 17/08/2026

➕➕➕ New Working Paper ➕➕➕ Fixed effects are the workhorse of management and economics for dealing with unobserved confounding. But what if they themselves create bias? 😲 A short thread 🧵 1/6

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Noah Greifer @noahgreifer.bsky.social · 03/08/2026

I'm so happy to announce version 2.0.0 of my #Rstats package WeightIt is out on CRAN! New features: censoring weights, multilevel propensity scores, improved weights for continuous treatments, bias-reduced ordinal and multinomial models, M-estimation in subgroups Check out the website below!

ngreifer.github.io

Weighting for Covariate Balance in Observational Studies

Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality ...

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Macartan Humphreys @macartan.bsky.social · 03/08/2026

Replicate everything? Interested in thoughts on this idea: shiny2.wzb.eu/ipi/replicate/ At @wzb.bsky.social we've been developing a protocol for replication files for empirical papers to: ✅be readable by machines ✅keep humans in the loop

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Macartan Humphreys @macartan.bsky.social · 23/06/2026

Slides from recent talks and courses on a bunch of topics: macartan.github.io/teaching/sli... replication, experimental design, survey experiments mixed methods... human shields, community driven reconstruction, school sorting, crises, selection and incentives...

macartan.github.io

Recent presentatons | Macartan Humphreys

Macartan Humphreys

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Vincent Arel-Bundock @vincentab.bsky.social · 20/06/2026

🎉 Announcing Calepin 🎉 A new tool to create computational notebooks and static websites, built on Typst. Write code and prose directly in your .typ files, then publish them as documents or as a full website. vincentarelbundock.github.io/calepin/ 1/6 🧵 #Rstats #Pydata #Typst

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Yiqing Xu @yiqingxu.bsky.social · 05/06/2026

1/ Happy to share that "Factorial Difference-in-Differences" (FDID), with Anqi Zhao and @pengding00 , is out in JASA - ACS. doi.org/10.1080/0162... It has been a truly thrilling experience working with Anqi and Peng.

doi.org

Factorial Difference-in-Differences

We formulate factorial difference-in-differences (FDID), a research design that extends canonical difference-in-differences (DID) to settings in which an event affects all units. In many panel data...

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Carl Müller-Crepon @carlmc.bsky.social · 22/05/2026

So exciting to see my paper "Ethnic Geography and the Colonial Design of Administrative Units in Sub-Saharan Africa" out in @cpsjournal.bsky.social ! Administrative regions and districts are important but why did (colonial) rulers shape them the way they did? A 🧵 journals.sagepub.com/doi/10.1177/...

journals.sagepub.com

Ethnic Geography and the Colonial Design of Administrative Units in Sub-Saharan Africa - Carl Müller-Crepon, 2026

Subnational administrative units are fundamental to territorial states and their political topography, but we know little about how their borders are designed. ...

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Julian Walterskirchen @jwalterskirchen.bsky.social · 27/04/2026

Our recent paper has been published in @intlinteractions.bsky.social Taking time seriously: Predicting conflict fatalities using temporal fusion transformers This paper documents our contribution to the GFFO / VIEWS Prediction Challenge on forecasting organized violence.

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Seva @seva.bsky.social · 15/04/2026

a deep dive into many bad predictions, and a few good ones, and what they mean for thinking about future conflict

hegemon.substack.com

The Expert Trap and the Next War

what forecasting failure tells us about the future

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Yiqing Xu @yiqingxu.bsky.social · 27/03/2026

1/🧵 A major update to our paper: "Scaling Reproducibility" w/ Leo Yang [Cross-posted from X] We move beyond reanalyzing a single design to (almost) full-paper replication! Paper: bit.ly/repro-ai

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EPSS @epssnet.bsky.social · 04/03/2026

EPSS Travel Grants for 2026 Belfast conference: PhD students & junior scholars @ institutions in European countries that are often underrepresented, can apply for full fee waiver & £500 stipend Apply: lnkd.in/ehXjhCgf Deadline: March 8, 11:59pm GMT. More details: lnkd.in/eZTP5sWR

lnkd.in

This link will take you to a page that’s not on LinkedIn

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Elizabeth N. Saunders @profsaunders.bsky.social · 04/03/2026

🧵Cans of Worms possibly opened by Trump's war of choice in Iran, a running list: (not exhaustive, with the caveat that I am not an expert on the region so these are questions from a US foreign policy perspective, please add others) 1/?

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Joshua Tucker @jatucker.bsky.social · 04/03/2026

Are you a social scientist who has been hearing about Claude Code but hasn't checked it out yet? Or are you a social scientist who has been playing with Claude Code and has been wondering what it means for the future of social science research? www.brookings.edu/articles/the... 1/

brookings.edu

The train has left the station: Agentic AI and the future of social science research | Brookings

A new era of agentic AI agents has begun. What does it mean for social scientists? Solomon Messing and Joshua Tucker discuss.

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Moritz Marbach @moritz-marbach.com · 27/02/2026

🧺 Paper Picnic 2.0 is here! More journals. New features. An easier way to keep up with the latest research in political science and adjacent fields. 🧵👇

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Yiqing Xu @yiqingxu.bsky.social · 18/02/2026

1/ Sorry for double-posting from X. Sharing a new working paper for the Year of the Horce 🐎: "An AI-assisted workflow that scales reproducibility in empirical research" (bit.ly/repro-ai) w/ Leo Yang Yang

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Adam Scharpf @ascharpf.bsky.social · 16/02/2026

Russia, Venezuela, Iran, China, the Sahel region, the United States ... Want to know why state agents carry out brutal repression — or participate in illegal coups? Our new book "Making a Career in Dictatorship" provides answers — it just got published by @academic.oup.com: tinyurl.com/ystwm3tf

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Ryan Briggs @ryancbriggs.net · 11/02/2026

I have a new paper. We look at ~all stats articles in political science post-2010 & show that 94% have abstracts that claim to reject a null. Only 2% present only null results. This is hard to explain unless the research process has a filter that only lets rejections through.

It must be very hard to publish null results
Publication practices in the social sciences act as a filter that favors statistically significant results over null findings. While the problem of selection on significance (SoS) is well-known in theory, it has been difficult to measure its scope empirically, and it has been challenging to determine how selection varies across contexts. In this article, we use large language models to extract granular and validated data on about 100,000 articles published in over 150 political science journals from 2010 to 2024. We show that fewer than 2% of articles that rely on statistical methods report null-only findings in their abstracts, while over 90% of papers highlight significant results. To put these findings in perspective, we develop and calibrate a simple model of publication bias. Across a range of plausible assumptions, we find that statistically significant results are estimated to be one to two orders of magnitude more likely to enter the published record than null results. Leveraging metadata extracted from individual articles, we show that the pattern of strong SoS holds across subfields, journals, methods, and time periods. However, a few factors such as pre-registration and randomized experiments correlate with greater acceptance of null results. We conclude by discussing implications for the field and the potential of our new dataset for investigating other questions about political science.

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Florian Hollenbach @fhollenbach.org · 03/02/2026

🧵 New version of our paper (@bcegerod.bsky.social) is finally online: "How Many is Enough? Sample Size in Staggered Difference-in-Differences Designs" We show that even well-identified DiD studies are often underpowered; sample sizes needed are surprisingly large Paper: osf.io/preprints/os... 1/6

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Dirk Hovy @dirkhovy.bsky.social · 20/01/2026

🚨(Software) Update: In my PhD, I had a side project to fix an annoying problem: when you ask 5 people to label the same thing, you often get different answers. But in ML (and lots of other analyses), you still need a single aggregated answer. Using the majority vote is easy–but often wrong. 1/N

github.com

GitHub - dirkhovy/MACE: Multi-Annotator Competence Estimation tool

Multi-Annotator Competence Estimation tool. Contribute to dirkhovy/MACE development by creating an account on GitHub.

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Sebastian van Baalen @sebastianvanbaalen.se · 14/01/2026

📦 I got tired of constantly battling TikZ or Word for decent causal diagrams, so decided to do something about it. Here is a first public version of 𝗰𝗮𝘂𝘀𝗮𝗹𝗽𝗹𝗼𝘁, an R package for creating clean, publication-ready causal diagrams with minimal effort. #rstats github.com/sebastianvan...

github.com

GitHub - sebastianvanbaalen/causalplot: Create Causal Diagrams Using A Simple Convencience Function

Create Causal Diagrams Using A Simple Convencience Function - sebastianvanbaalen/causalplot

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Darren Wilkinson @darrenjw.bsky.social · 01/01/2026

Some lecture notes on spatial and temporal modelling, illustrated using R - darrenjw.github.io/spatio-tempo... - #rstats #rspatial #quarto

darrenjw.github.io

Spatial and temporal statistics

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apoorva lal @handle.invalid · 01/01/2026

Life cycle of a method 1. scrappy theory 2. killer empirical application (Angrist/Krueger for IV, Card/Krueger for DID, Lee for RD etc) 3. Deep, general theory 4. Review papers 5. Bad empirical applications 6. Empirical reviews Proximal has skipped 2 entirely. Is this typical for epi methods?

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ᴅʀ. ᴍᴏᴡɪɴᴄᴋᴇʟ'ꜱ @drmowinckels.io · 28/12/2025

My #RPackageAdvent2025 recap is live! 📦✨ Get the complete guide to modern R package development: setup, docs, testing, CI/CD, and CRAN submission. All 25 days of tips, tools, and best practices in one place! Dive in! #RStats #RPackageDevelopment Link: drmo.site/8ny3eU

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Andrew Perfors @perfors.net · 11/12/2025

Hey #rstats and #stats people, does anyone know of any references that explain the hazards of trying to run or interpret models with all combinations of many predictors? (i.e., including multiple 2-way and 3-way interactions)

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Richard McElreath 🐈‍⬛ @rmcelreath.bsky.social · 09/12/2025

I'm teaching Statistical Rethinking again starting Jan 2026. This time with live lectures, divided into Beginner and Experienced sections. Will be a lot more work for me, but I hope much better for students. I will record lectures & all will be found at this link: github.com/rmcelreath/s...

course schedule as a table. Available at the link in the post.

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Carl Müller-Crepon @carlmc.bsky.social · 06/12/2025

🚨Jobs!🚨 3-year PostDoc positions (aka Research Officers) at @lsegovernment.bsky.social to work with me on the local consequences of border change. Please reach out for questions and apply by Jan 4th to join the team and department: I’d love to hear from you! Job ad: jobs.lse.ac.uk/Vacancies/W/...

jobs.lse.ac.uk

Research Officer

Research Officer, , <p style="background: white; margin: 0cm; text-align: center;"><em><span style="color: #333333;">LSE is committed to building a diverse, equitable and truly inclusive university</s...

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SocArXiv @socarxiv.bsky.social · 27/11/2025

In light of record submission rates and a large volume of AI-generated slop, SocArXiv recently implemented a policy requiring ORCIDs linked in the OSF profile of submitting authors, and narrowing our focus to social science subjects. Today we are taking two more steps: /1

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Sabine Carey @sabinecarey.bsky.social · 21/11/2025

📣 out now, with @argohdes.bsky.social and Mark Gibney - and in honour of the wonderful Steve Poe, who motivated the 1st edition with @universitypress.cambridge.org tinyurl.com/2s4kh6z3. If you enjoy reading this even a fraction as much as I enjoyed creating it, you’re in for a ride 🤩

cambridge.org

The Politics of Human Rights | Cambridge Aspire website

Discover The Politics of Human Rights, 2nd Edition, Sabine C. Carey, HB ISBN: 9781009537919 on Cambridge Aspire website

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Andrew Heiss @andrew.heiss.phd · 17/11/2025

I recently discovered Conventional Comments (conventionalcomments.org) for providing a pseudo-standard set of labels for feedback and just tried it for an article review and it was really helpful to specify issues vs. thoughts vs. suggestions, etc. Hopefully it's helpful for the authors too!

We strongly suggest using the following labels:

praise: Praises highlight something positive. Try to leave at least one of these comments per review. Do not leave false praise (which can actually be damaging). Do look for something to sincerely praise.
nitpick: Nitpicks are trivial preference-based requests. These should be non-blocking by nature.
suggestion: Suggestions propose improvements to the current subject. It’s important to be explicit and clear on what is being suggested and why it is an improvement. Consider using patches and the blocking or non-blocking decorations to further communicate your intent.
issue: Issues highlight specific problems with the subject under review. These problems can be user-facing or behind the scenes. It is strongly recommended to pair this comment with a suggestion. If you are not sure if a problem exists or not, consider leaving a question.
todo: TODO’s are small, trivial, but necessary changes. Distinguishing todo comments from issues: or suggestions: helps direct the reader’s attention to comments requiring more involvement.
question: Questions are appropriate if you have a potential concern but are not quite sure if it’s relevant or not. Asking the author for clarification or investigation can lead to a quick resolution.
thought: Thoughts represent an idea that popped up from reviewing. These comments are non-blocking by nature, but they are extremely valuable and can lead to more focused initiatives and mentoring opportunities.
chore: Chores are simple tasks that must be done before the subject can be “officially” accepted. Usually, these comments reference some common process. Try to leave a link to the process description so that the reader knows how to resolve the chore.
note: Notes are always non-blocking and simply highlight something the reader should take note of.

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Petter Törnberg @pettertornberg.com · 07/11/2025

LLMs are now widely used in social science as stand-ins for humans—assuming they can produce realistic, human-like text But... can they? We don’t actually know. In our new study, we develop a Computational Turing Test. And our findings are striking: LLMs may be far less human-like than we think.🧵

arxiv.org

Computational Turing Test Reveals Systematic Differences Between Human and AI Language

Large language models (LLMs) are increasingly used in the social sciences to simulate human behavior, based on the assumption that they can generate realistic, human-like text. Yet this assumption rem...

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Jason "Red5" Lyall @jaylyall.bsky.social · 07/11/2025

🚨New paper🚨: Are you interested in key debates about civil war and counterinsurgency? Like questions about causal inference but wish you had a better method for dealing with spillover and carryover effects? Then check out our new paper: arxiv.org/abs/2504.03464

arxiv.org

Spatiotemporal causal inference with arbitrary spillover and carryover effects: Airstrikes and insurgent violence in the Iraq War

Social scientists now routinely draw on high-frequency, high-granularity ''microlevel'' data to estimate the causal effects of subnational interventions. To date, most researchers aggregate these data...

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Nils Weidmann @nilsweidmann.bsky.social · 29/10/2025

Thanks to the great help by @rstrauch.bsky.social, Gabriele Spilker and the Cluster of Excellence "The Politics of Inequality" @excinequality.bsky.social, version 5.0 of the "Mass Mobilization in Autocracies Database" (coverage until 2022) is now available at mmadatabase.org!

mmadatabase.org

Mass Mobilization in Autocracies Database

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Alexander Wuttke @kunkakom.bsky.social · 13/10/2025

Colleagues: What’s your favorite, accessible resource (reading, video, slide deck) to convince students that regression on observational data—even with many controls—is not often causal. (Quasi-)experimental designs are preferred for causal inference.

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Julia M. Rohrer @dingdingpeng.the100.ci · 13/10/2025

New blog post! Let's say you have two measures meant to capture the same confounder. They're highly correlated. Can you still proceed with your regression analysis? (I admit, the title is a bit of a spoiler) www.the100.ci/2025/10/13/i...

the100.ci

If you have two measures of the same confounder, you can just include both of them in your regression model

Sometimes, researchers worry about multicollinearity in situations where it’s actually a non-issue. Here’s one such scenario. Imagine a situation where you are interested in the effect of X on Y (X...

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Felix Wiebrecht @felixwiebrecht.bsky.social · 08/10/2025

New Working Paper 🚨 I am introducing the new dataset on Parliamentary Elites in Authoritarian Regimes (PEAR). It includes information on the composition of parliaments in 819 legislative terms in 130 autocracies from 1945 - based on over 200k individual-level observations. tinyurl.com/48h68hmh

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Xu Xu @xuxupolitics.bsky.social · 06/10/2025

Why do authoritarian states charge political opponents with non-political crimes? In our @thejop.bsky.social paper with Jennifer Pan & @yiqingxu.bsky.social, we examine how *Disguised Repression* undermines opponents’ moral authority and mobilization capacity. doi.org/10.1086/7342...

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Nicolai Berk @nicolaiberk.bsky.social · 29/09/2025

I had the pleasure to teach a 3-day crash course on #LLMs for PhD students. We covered: 1️⃣ Text Representation and Embeddings 2️⃣ Machine Learning & Transformer Architecture 3️⃣ Generative Models for Social Sciences The 6 slide sets and 10 notebooks are available on github: github.com/nicolaiberk/...

github.com

GitHub - nicolaiberk/llm_ws: Materials for my Workshop on LLMs

Materials for my Workshop on LLMs. Contribute to nicolaiberk/llm_ws development by creating an account on GitHub.

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Vincent Arel-Bundock @vincentab.bsky.social · 29/09/2025

{tinytable} 0.14.0 for #RStats makes it super easy to draw tables in html, tex, docx, typ, md & png. There are only a few functions to learn, but don't be fooled! Small 📦s can still be powerful. Check out the new gallery page for fun case studies. vincentarelbundock.github.io/tinytable/vi...

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Laurent Bergé @lrberge.bsky.social · 10/09/2025

#rstats #econsky fixest v0.13.0 is finally out! It's still about making OLS and GLM estimations easy. Some major changes: - *default* VCOV becomes iid always! - singletons are removed by default! See all the changes here: github.com/lrberge/fixe...

cran.r-project.org

fixest: Fast Fixed-Effects Estimations

Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the p...

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Andrea Ruggeri @andrearuggeri.bsky.social · 09/09/2025

Thanks to @jpeaceresearch.bsky.social for giving us the possibility to guest edit a special issue on "Political violence in democracies". Ursula Daxecker, @neerajprsd.bsky.social and I wrote an open access introduction: journals.sagepub.com/doi/full/10.... Thanks to all authors of 14 articles!

journals.sagepub.com

Political violence in democracies: An Introduction - Andrea Ruggeri, Ursula Daxecker, Neeraj Prasad, 2025

It is well established that democracies experience less political violence than autocracies. Paradoxically, however, this widely accepted fact has led scholars ...

02010

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Noah Greifer @noahgreifer.bsky.social · 09/09/2025

I recently updated my #Rstats package {optweight} for the first time in 6 years(!), and I want to give it the attention it deserves with an announcement and mini thread. In short, {optweight} uses optimization to estimate balancing weights in observational studies. #episky #statssky #causalsky 1/

ngreifer.github.io

Optimization-Based Stable Balancing Weights

Use optimization to estimate weights that balance covariates for binary, multi-category, continuous, and multivariate treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>...

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ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 26/08/2025

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

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Julia M. Rohrer @dingdingpeng.the100.ci · 25/08/2025

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...

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.

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Allard Duursma @allardduursma.bsky.social · 22/08/2025

Armed conflicts involve governments, rebel groups, militias, communal factions, and foreign forces — all interacting in complex ways. The result? A web of violence that can feel overwhelming to understand. That's why we launch the Conflict Networks Explorer peaceobservatory.com/conflict-networks

peaceobservatory.com

Peace Observatory

Peace observatory website.

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Aidan Milliff @aidanmilliff.com · 20/08/2025

🚨 Just accepted at @bjpols.bsky.social! Paolo Bertolotti, Fotini Christia, Ali Jadbabaie, and I use 12 billion Yemeni cellphone records to study the effects of U.S. drone strikes on civilian mobility and displacement. We Find: 🧵: 1/3

Screenshot of title page of article. Download pre-print version here: https://aidanmilliff.com/publication/estimating-the-impact-of-drone-strikes/BMCJ_Drones_20Aug25.pdf

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Allard Duursma @allardduursma.bsky.social · 21/08/2025

🚀 Exciting news: The Peace Observatory is live! 👉 peaceobservatory.com The Peace Observatory is now the world’s largest repository of peace negotiation summaries. It covers 11,000+ rounds of negotiations and 3,000 agreements across 1,300+ armed conflicts worldwide (1989–2023).

peaceobservatory.com

Peace Observatory

Peace observatory website.

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Gavin Simpson @gsimpson.bsky.social · 18/08/2025

🚀 gratia 0.11.0 is out! Now has a paper in JOSS — please cite 📄 doi.org/10.21105/jos... Experimental parallel processing ⚡ New assemble() for building plots 🎨 Better support for complex families + new diagnostics 🧪 Lots of bug fixes + polish ✨ 👉 gavinsimpson.github.io/gratia/ #Rstats

gavinsimpson.github.io

An R package for working with generalized additive models

Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package.

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