Statistical Modeling, Causal Inference, and Social Science

Statistical Modeling, Causal Inference, and Social Science

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Academic statistics blog frequently addressing clinical trial design/causal inference and evidence appraisal.

An Application for Training Deep Learning Models in Your Browser
NewsApr 9, 2026

An Application for Training Deep Learning Models in Your Browser

Jordan Anaya has launched a web application, aleaaxis.net, that enables users to train deep learning models directly in their browsers. The tool is positioned as an educational platform to introduce students to AI without requiring local installations. Early user feedback...

By Statistical Modeling, Causal Inference, and Social Science
Updike in Tehran
NewsApr 8, 2026

Updike in Tehran

The author reflects on John Updike’s two‑volume collected stories, highlighting the early‑2000s tale “The Varieties of Religious Experience.” The story uniquely frames the 9/11 attacks from multiple viewpoints, including an elderly Updike‑type narrator, a hijacker, and a plane passenger. While...

By Statistical Modeling, Causal Inference, and Social Science
My (Uninformed and Completely Speculative) Theory About Jeff Bezos and the Washington Post
NewsApr 6, 2026

My (Uninformed and Completely Speculative) Theory About Jeff Bezos and the Washington Post

In 2013 Jeff Bezos purchased The Washington Post, a move that sparked debate about billionaire motives in a shrinking newspaper market. While the acquisition offered prestige, a potential political shield, and a possible digital‑media upside, the paper has become a...

By Statistical Modeling, Causal Inference, and Social Science
How Do Political Organizations and Politically-Minded Rich People Translate Money Into Media Influence?  Differently than They Used To.
NewsApr 5, 2026

How Do Political Organizations and Politically-Minded Rich People Translate Money Into Media Influence? Differently than They Used To.

The article traces the evolution of political money flowing into media, from early partisan newspapers to modern right‑wing ownership of TV networks, digital outlets, and social platforms. It notes that the removal of the Fairness Doctrine and rising polarization enabled...

By Statistical Modeling, Causal Inference, and Social Science
Black and White, Gray and in Between: What Color Is the Media?
NewsMar 31, 2026

Black and White, Gray and in Between: What Color Is the Media?

The article examines the limited media coverage of ETH Zurich professor Tom Crowther’s tenure denial after bullying and harassment complaints, highlighting his use of lab funds for crisis‑communication services. It draws parallels to the reopened Noma restaurant under chef René Redzepi, noting...

By Statistical Modeling, Causal Inference, and Social Science
Frank Harrell on Why and How to Do Bayes for Clinical Trials and the Recent FDA Draft Guidelines
NewsMar 26, 2026

Frank Harrell on Why and How to Do Bayes for Clinical Trials and the Recent FDA Draft Guidelines

Frank Harrell, a former FDA statistician, responded to recent JAMA commentary on the agency’s draft guidance promoting Bayesian methods for clinical trials. He highlighted that while the guidance is a step forward, FDA reviewers still rely on traditional frequentist approaches...

By Statistical Modeling, Causal Inference, and Social Science
Claimed “100% Sensitivity and Specificity in Differentiating Autistic Individuals From Typically Developing Controls Using Retinal Photographs” . . . Yeah,...
NewsMar 21, 2026

Claimed “100% Sensitivity and Specificity in Differentiating Autistic Individuals From Typically Developing Controls Using Retinal Photographs” . . . Yeah,...

Two recent JAMA Network Open studies report near‑perfect diagnostic performance for autism using retinal photographs and video‑based deep‑learning models. The retinal study claims 100 % sensitivity and specificity across 958 participants, while the video study reports an AUC above 0.99. Critics...

By Statistical Modeling, Causal Inference, and Social Science
Survey Statistics: Individualism Doesn’t Work (Even when Weighted)
NewsMar 17, 2026

Survey Statistics: Individualism Doesn’t Work (Even when Weighted)

Multilevel regression‑poststratification (MRP) aims to estimate population means, but most machine‑learning pipelines still optimize an individual‑level loss, which measures error for each respondent. The true MRP objective is a population‑level loss, focusing on the aggregate mean rather than individual predictions....

By Statistical Modeling, Causal Inference, and Social Science
SparseNUTS: Preconditioning Hierarchical Models in HMC with a Sparse “Laplace Approximation” At the Marginal Mode
NewsMar 12, 2026

SparseNUTS: Preconditioning Hierarchical Models in HMC with a Sparse “Laplace Approximation” At the Marginal Mode

Researchers led by Cole Monnahan released SparseNUTS, an R package that preconditions Hamiltonian Monte Carlo using a sparse Laplace approximation at the marginal mode of hierarchical models. By leveraging the sparse precision matrix from TMB or lme4, the method replaces...

By Statistical Modeling, Causal Inference, and Social Science
New Course on Generative AI for Behavioral Science
NewsMar 10, 2026

New Course on Generative AI for Behavioral Science

Northwestern University introduced a graduate seminar titled "Generative AI for Social Science," merging computer science and communications students to examine how large language models can simulate human behavior. The course surveys emerging applications, methodological challenges, and metascientific concerns, culminating in...

By Statistical Modeling, Causal Inference, and Social Science
“The Idea of Israel” . . . More Generally, The Idea of X, for Different Values of X
NewsMar 9, 2026

“The Idea of Israel” . . . More Generally, The Idea of X, for Different Values of X

Ilan Pappe’s 2014 book *The Idea of Israel* chronicles the 1990s “post‑Zionist” surge in Israeli academia, arts and media that challenged the dominant patriotic narrative. The book argues that this brief period of critical scholarship was later curbed by a...

By Statistical Modeling, Causal Inference, and Social Science
Bayesian Inferences and Frequentist Evaluations
NewsMar 7, 2026

Bayesian Inferences and Frequentist Evaluations

Researchers Forster, Novelli, and Welch applied four frequentist and two Bayesian sequential designs to the COVID‑disrupted UK DISC clinical trial. All six approaches confirmed the trial’s original finding of treatment superiority but suggested different optimal points for restarting patient recruitment....

By Statistical Modeling, Causal Inference, and Social Science
Ethics Corner:  “As a Statistical Consultant, if You’re a Co-Author on a Substantive Paper, Is It Your Duty to...
NewsMar 4, 2026

Ethics Corner: “As a Statistical Consultant, if You’re a Co-Author on a Substantive Paper, Is It Your Duty to...

Statistical consultants who become co‑authors must balance honesty with scope. They should fully disclose the analyses they performed and any limitations, but are not obligated to fix every statistical flaw beyond their contract. If a manuscript contains questionable methods, the...

By Statistical Modeling, Causal Inference, and Social Science
From Junk Science (Largely Non-Political) to Junk Medical Treatments (Mostly Associated with the Far-Right):  A Financial Connection
NewsFeb 20, 2026

From Junk Science (Largely Non-Political) to Junk Medical Treatments (Mostly Associated with the Far-Right): A Financial Connection

Paul Krugman highlights a growing financial link between the multi‑billion‑dollar wellness industry and right‑wing extremist movements. He notes that U.S. spending on wellness reaches roughly $500 billion annually, with nutritional supplements alone accounting for about $70 billion, while regulators like the FDA...

By Statistical Modeling, Causal Inference, and Social Science