Jacob Edenhofer

@jacobedenhofer.bsky.social

BA, PPE @warwickuni / MPhil, Comparative Government @UniofOxford / DPhil student in Politics @NuffieldCollege & @Politics_Oxford Link to my blog “Often wrong, but sometimes useful”: https://jacobedenhofer.substack.com/

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!

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 ...

ngreifer.github.io

Excited to share a new working paper with Vincent Abraham and Catarina Marvão: Using the population of legal cartels in Sweden, we provide the first empirical evidence that common ownership can facilitate explicit collusion. www.nber.org/papers/w35565

Common Ownership and Collusion

Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, an...

nber.org

"We're all worried," as what it means to do research (in my field, Theoretical CS) seems to be shifting, and shifting fast. What to do? Senior researchers must lead by example, knowing that not everything will pan out. What I'm suggesting below may not work everywhere, but here's my own advice: 1/

I have read this Response to my Comment and have written a Rejoinder, showing three things: 1. The multiverse is many correlated tests. 2. The randomization inference is wrong. 3. The trichotomous moderator reaffirms my point. I also enumerate 7 misleading claims, now in print in the APSR.

A Rejoinder to ‘Still Instrumentally Inclusive’ (Turnbull-Dugarte & López Ortega)
Daniel de KadtCornell University, Department of Governmentdekadt@cornell.edu
2026-08-01
Abstract Turnbull-Dugarte and López Ortega’s “Still Instrumentally Inclusive” (the Response) answers my replication (the Comment) of their 2024 American Political Science Review paper (the Article) with, among other things, a multiverse of 2,970 analyses based on eighteen different weighting schemes, randomization inference, and a trichotomous moderator. As I show in this rejoinder, each is undermined by bona fide errors in their code, their writing, and their interpretation. First, the multiverse is just many correlated variants of the same test. This is in large part due to the weighting schemes which suffer multiple problems (some due to code errors, some intrinsic), and are largely correlated variations on one weighting scheme. Second, due to code errors the randomization inference compares a weighted statistic against an unweighted null and, in the two heterogeneity tests, permutes the interaction column rather than treatment assignment column. Every permutation p-value the Response reports is therefore computed against the wrong reference distribution; when they are recomputed correctly, two of the five it reports as significant are no longer so, and they are the two the Response emphasizes. Third, the trichotomous moderator is erroneously described as terciles, but it is in fact based on non-tercile cutpoints. The key subgroup effect reported in the Response thus rests on comparing just thirty high-weight control respondents against thirty-one high-weight treated respondents, and ends up reaffirming my original point that the heterogeneity in study 2 is with respect to weights, and not immigration sentiment as their theory predicts. I close by documenting seven claims in the Response that are untrue or misleading.
Cambridge University Press Political Science & IR@cambup-polsci.cambridge.org · last wk.

#OpenAccess from @apsrjournal.bsky.social - Still Instrumentally Inclusive - https://cup.org/4bhrhsp "Do individuals in Western democracies shift their views on LGBT+ inclusion when exposed to opposition from Muslim out-groups?" - @turnbulldugarte.com & @bertous.bsky.social #FirstView

Banner with the hashtag #OpenAccess on a green background and the text "American Political Science Review" on a blue background below.

New from me: Until recently, the gap between comfortably-off and just-getting-started could plausibly be crossed in a decade or two of hard work. Growth in asset prices means that’s no longer true: even a lifetime of striving doesn’t offer a realistic prospect of feeling you’ve “made it”.

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🚨New paper Like most things in democracies, achieving climate action in the form of sustainable policy needs a significant amount of political mobilization. We investigate when & where this is more likely to happen across places on the climate frontline: the Global South🧵 📌 osf.io/preprints/so...

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It was such a pleasure working with Ashwini, Luke, and Isaias on this project over the past few years. They are a lot of fun, take the work seriously, have great values, and are seriously awesome people. 🧵👇

Ashwini Ashokkumar@ashwinia.bsky.social · 5d ago

New paper in @nature.com with @lukebeehewitt.bsky.social, @isaiasghezae.bsky.social, & @robbwiller.bsky.social: Can LLMs predict results of social science experiments? Across 70 experiments, we find a strong correspondence (r=.85) between predicted and observed effects. doi.org/10.1038/s415... 🧵

I think this paper in part reproduces a measure of "intra-party unequal representation" found in "A unified approach to measuring unequal representation" doi.org/10.1007/s111... which very unfortunately is not cited

A unified approach to measuring unequal representation - Public Choice

The concept of unequal representation is commonly understood through the lenses of disproportionality and malapportionment, pertaining to inter-party and inter-district aspects, respectively. Popular indices used to measure such features are analyzed separately despite being mathematically identical. District-level wasted votes are not measured in terms of unequal representation, even though they can be conceptualized as intra-district unequal representation. A new component, intra-party unequal representation, which measures unequal representation across districts for voters who support each party, has not been considered to contribute to unequal representation. We propose a unified approach for measuring these components—disproportionality, malapportionment, wasted votes, and intra-party unequal representation—by using $${\alpha }$$ α -divergence. We show mathematically that the total of disproportionality and intra-party unequal representation equals that of malapportionment and wasted votes. We apply this approach to the Japanese political system and demonstrate the role of intra-party unequal representation in sustaining disproportionality in favor of the Liberal Democratic Party.

doi.org

British Journal of Political Science@bjpols.bsky.social · last wk.

NEW - Disproportionality in Geographic Representation - https://cup.org/4fLxlL5 - Orit Kedar, Yair Amitai & Gilad Hurvitz #OpenAccess

BJPolS abstract discussing geographic representation in elections. Highlights geographic disproportionality between votes and party seats, using data from 113 districts across twelve democracies.

One last thing: this project was incredibly fun to work on since we got to put Kirill and Peter's "formula instrument" technology to work on an **actual** formula! Seeing that the admin's was literally doing a shift-share was a super exciting research moment! onlinelibrary.wiley.com/doi/abs/10.3...

Econometrica | Econometric Society Journal | Wiley Online Library

We develop a new approach to estimating the causal effects of treatments or instruments that combine multiple sources of variation according to a known formula. Examples include treatments capturing ...

onlinelibrary.wiley.com

A solid critique of over claiming causal inference with DiD. Just because you can show a stable difference between groups doesn't mean that you know which treatment caused it. That's a counterfactual question that DiD *assumes away.* I.e., DiD is really another form of "model-based inference".

Elizabeth Nolan Brown@enbrown.bsky.social · last wk.

Remote work is a feminist issue and this study (and NYT op-ed) panning it are just plain silly reason.com/2026/07/27/n...