Adam Bouyamourn

I am an Assistant Professor and Faculty Fellow at the Center for Data Science at New York University.

I study the social implications of AI systems, and develop new methods for researchers in the social sciences.

Before NYU, I was a Postdoctoral Research Associate in Politics, Statistics and Machine Learning at Princeton University, where I worked with Arthur Spirling. I completed my PhD in Political Science at UC Berkeley, supervised by Thad Dunning.

Before graduate school I was a journalist, at The Guardian (London) and The National (Abu Dhabi).

I am British and Moroccan, and a lifelong chorister.

Photo of Adam Bouyamourn
CV PDF
Three bars from Britten's Rejoice in the Lamb setting the word 'variance'
Benjamin Britten, Rejoice in the Lamb (1943).
Research
Area
Method

Writing samples

  1. Adam Bouyamourn. “Experimental Site Selection Under Directional Distribution Shifts.” Working paper, 2026. pdf ASA Best Student Paper Award Methodologycausal inferenceoptimization
  2. Adam Bouyamourn, Arthur Spirling. “Interpretable Aggregation of Correlated LLM Annotators.” Working paper, 2026. pdf Methodologycausal inferencemachine learning

Publications

  1. Clara Bicalho, Adam Bouyamourn, Thad Dunning. “The Power of Prognosis: Improving Covariate Balance Tests with Outcome Information.” Political Analysis, 2026. Methodologycausal inference
  2. Andrés Cruz, Adam Bouyamourn, Joseph Ornstein. “Survey Quality and Acquiescence Bias: A Cautionary Tale.” Political Analysis, 2026. Methodology
  3. Adam Bouyamourn, Alexander Williams Tolbert. “Escaping the Subprime Trap in Algorithmic Lending.” Foundations of Responsible Computing (FORC), 2026. AI and Societygame theory and mechanism designmachine learning
  4. Center for AI Safety, Scale AI & Humanity's Last Exam contributors (incl. Adam Bouyamourn). “A Benchmark of Expert-Level Academic Questions to Assess AI Capabilities.” Nature, 2026. AI and Societymachine learning
  5. Balazs Aczel et al. (incl. Adam Bouyamourn). “Investigating the Analytical Robustness of the Social and Behavioral Sciences.” Nature, 2026. Methodology
  6. Adam Bouyamourn. “Collusive and Adversarial Replication.” Research & Politics, 2025. Methodologygame theory and mechanism design
  7. Adam Bouyamourn. “Why LLMs Hallucinate, and How to Get (Evidential) Closure: Perceptual, Intensional and Extensional Learning for Faithful Natural Language Generation.” EMNLP, 2023. AI and Societycausal inferencemachine learning
  8. Lucas Spangher, Akash Gokul, Manan Khattar, Joseph Palakapilly, Akaash Tawade, Adam Bouyamourn, Alex Devonport, Costas Spanos. “Prospective Experiment for Reinforcement Learning on Demand Response in a Social Game Framework.” ACM e-Energy, 2020. Methodologycausal inferencemachine learning