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Jasjeet Sekhon's Website
webjsekhon.com·jsekhon.com
Sekhon's causal inference methods may be useful for AI safety researchers conducting empirical policy evaluations or studying intervention effects in AI deployment contexts where controlled experiments are not possible.
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Summary
Personal academic website of Jasjeet Sekhon, a professor specializing in causal inference, matching methods, and statistical methodology applied to social sciences and policy. His work on observational study methods and causal identification is relevant to empirical AI safety research and policy evaluation. The site serves as a hub for his research, publications, and software tools.
Key Points
- •Sekhon develops causal inference methods including matching algorithms used to estimate treatment effects in observational data
- •His GenMatch and MatchIt tools are widely used in empirical policy and social science research
- •Research focuses on rigorous statistical methodology for drawing causal conclusions from non-experimental data
- •Work is relevant to AI governance and policy evaluation where randomized experiments are infeasible
- •Bridges statistics and political science with applications to public policy analysis
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Bridgewater AIA Labs | Organization | 66.0 |
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Jasjeet S. Sekhon - Yale University
Jasjeet S. Sekhon
Incoming Chief Strategy Officer, Google DeepMind
Former Chief Scientist and Head of AI, Bridgewater Associates
Former Professor at Yale , UC Berkeley , and Harvard
Software
Rforestry
Random Forests, Linear Trees, and Gradient Boosting for Inference and Interpretability
Causal Toolbox
Estimating Heterogeneous Treatment Effects
Matching
Multivariate and Propensity Score Matching Software for Causal Inference
rgenoud
GENetic Optimization Using Derivatives for nonlinear optimization problems
Selected Reprints & Working Papers
"AIA Forecaster: Technical Report." 2025.
"Establishing
Best Practices for Building Rigorous Agentic
Benchmarks." 2025.
"The Silent
Majority: Demystifying Memorization Effect in the
Presence of Spurious Correlations." 2025.
"A Framework to
Assess the Persuasion Risks Large Language Model Chatbots
Pose to Democratic Societies." 2025.
"Expert
of Experts Verification and Alignment (EVAL) Framework for
Large Language Models Safety in Gastroenterology."
2025.
"Human-Algorithmic Interaction Using a Large Language Model-Augmented Artificial Intelligence Clinical Decision Support System." CHI 2024.
"GutGPT: Novel Large Language Model Pipeline Outperforms Other Large
Language Models in Accuracy and Similarity to International Experts
for Guideline Recommended Management of Patients." Gastroenterology 2024.
"Calibrating
Multi-modal Representations: A Pursuit of Group Robustness
without Annotations." CVPR 2024.
"Enhancing Collaborative Medical Outcomes through Private Synthetic Hypercube Augmentation: PriSHA." PMLR 248:55-71, 2024.
"Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator." Dennis Shen, Dogyoon Song, Peng Ding, Jasjeet S. Sekhon.
"Assessing the Usability of GutGPT: A Simulation Study of an AI Clinical Decision Support System for Gastrointestinal Bleeding Risk."
"Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data." With Dennis
Shen, Peng Ding, and Bin Yu. Econometrica
"ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast." Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, Jasjeet S. Sekhon, James S. Duncan. MICCAI 2023
"Nonparametric identification is not enough, but randomized controlled trials are" With P. M. Aronow, James M. Robins, Theo Saarinen, and Fredrik
Savje. Observational Studies.
"Uniform,
nonparametric, non-asymptotic confidence sequences." With Steve Howard , Aaditya Ramdas , and Jon McAuliffe . Annals of Statistics.
"Time-uniform Chernoff bounds via nonnegative supermartingales." With Steve Howard , Aaditya Ramdas , and Jon McAuliffe . Probability Surveys.
"Linear Aggregation in Tree-based Estimators." With Sören Künzel, Theo Saarinen, and Edward Liu. Journal of Comp
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