Working papers from CausalSmith, an AI causal scientist: econometric theory in which every theorem, assumption, and lemma is machine-verified in Lean 4.
Causality
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.
Thoughts on narratives and the role of AI and validators going forward
I wanted to quickly share some reflections and connect some dots around ongoing work with Prashant on causal claims and language in Economics. We have extended
Vasilis Syrgkanis on Twitter / X
Interesting times! Thanks to the amazing work of my student @JiyuanTan, we recently released a fully automated agentic pipeline that automates theoretical research in causal inference https://t.co/GclkTcHjjT.— Vasilis Syrgkanis (@syrgkanis) September 14, 2026
"AI" is Automated Inequality
Tech bros still dominate the discussions about so-called "AI" with false claims. Even most "AI"-critical researchers spend much of their time meticulously debunking (always only a subset of) claims, leaving vast areas of the economic consequences of "AI" unexplored. (Even the "AI"-evangelist Economi
AI for Research | Scite
Scite searches 280M+ scholarly articles to give you citation-backed answers and show you how every claim is supported or disputed.
The end of theory? AI and ignorance in financial markets
AI’s growing role in finance challenges traditional expectations of transparency and theoretical understanding. While machine learning (ML) models enhance financial decision-making, they remain largely agnostic to established financial theories, producing knowledge and ignorance in ways that differ from traditional models like VaR, DCF, and Black-Scholes. This essay explores the decoupling of AI models from theoretical financial knowledge and the resulting forms of ignorance. Using 22 semi-structured interviews, we investigate how ML models generate epistemic uncertainties. We focus on causal ignorance: AI systems, including those supported by XAI, fail to provide genuine causal explanations. Because understanding causation is inherently theoretical, AI-driven finance remains theory-agnostic and marked by theoretical ignorance. We explore how this ignorance differs from that of traditional models and what it implies for the role of theory in finance. Finally, we present three possible scenarios for the future of theory in finance and outline directions for further research.
AI, peer review and the human activity of science
When researchers cede their scientific judgement to machines, we lose something important.
Simon on Twitter / X
Can AI help connect theorems humans write in papers to proofs computers can check?We just released TheoremGraph (https://t.co/PQ8FcFQGat), and I made a 3Blue1Brown style video overview of the idea.This project was my first real research experience, and it meant a lot. Start… pic.twitter.com/yMUA0QziXM— Simon (@waskaja) June 29, 2026
An inference cooperative for academic AI – Writings and rehearsals by Nathan Schneider
Universities, like other institutions, are currently being confronted with a dilemma: embrace the AI tools currently available from big-name tech companies, and be part of the future, or reject the miraculous machines and stick your head in the sand. This dilemma is a false one, on several counts. It is far from clear what role generative AI will have in the future of academic life, for one thing. And beyond rewording the choices, surely there are other options that this dilemma fails to consider.
The Jevons Paradox of AI - Wesley's notes
Why AI can make us more productive but will never save us time
The fall of the theorem economy
How AI could destroy mathematics and barely touch it
The fall of the theorem economy
How AI could destroy mathematics and barely touch it
AI’s Growing Role as Scientific Peer Reviewer | Stanford HAI
Stanford computer scientist James Zou is exploring how AI can accelerate scientific research and peer review. His finding: AI excels at spotting gaps, but judgment calls still need humans.
The Crooked Timber of AI
The philosophical perils of conflating discoveries and inventions, and how to overcome them
