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Sendhil Mullainathan

  1. Evaluating the World Model Implicit in a Generative Model
    2024/06/06 by Keyon Vafa, Justin Y. Chen, Vafa, Keyon +7 · 18 voices · 25 citations
    #cs.CL #cs.AI
  2. Discrimination in the Age of Algorithms
    2019/02/11 by Jon Kleinberg, Jens Ludwig, J. Ludwig +6 · 3 voices · 5 citations
    Computer Science · Social Sciences · #Discrimination and Equality Law #Human Rights and Immigration #Law, AI, and Intellectual Property #cs.AI #cs.CY #cs.LG
  3. How Much Should We Trust Differences-In-Differences Estimates?
    2004/02/01 by M. Bertrand, E. Duflo, Esther Duflo +2 · 163 citations
    Economics, Econometrics and Finance · Social Sciences · Mathematics · #Spatial and Panel Data Analysis #Income, Poverty, and Inequality #Advanced Causal Inference Techniques
  4. Potemkin Understanding in Large Language Models
    2025/06/26 by Marina Mancoridis, Bec Weeks, Mancoridis, Marina +5 · 28 voices · 7 citations
    Computer Science · Social Sciences · #Computational and Text Analysis Methods #Explainable Artificial Intelligence (XAI) #Topic Modeling #cs.AI #cs.CL
  5. What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
    2025/07/09 by Keyon Vafa, Vafa, Keyon, Peter G. Chang +5 · 17 voices · 12 citations
    Computer Science · Physics and Astronomy · #Historical Astronomy and Related Studies #Multimodal Machine Learning Applications #Time Series Analysis and Forecasting #cs.AI #cs.LG
  6. Prediction Policy Problems
    2015/05/01 by Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan +1 · 49 citations
    Mathematics · Economics, Econometrics and Finance · #Advanced Causal Inference Techniques #Health Systems, Economic Evaluations, Quality of Life #Healthcare Policy and Management
  7. Inherent Trade-Offs in the Fair Determination of Risk Scores
    2016/09/19 by Jon Kleinberg, Sendhil Mullainathan, Kleinberg, Jon +3 · 32 citations
    Computer Science · Arts and Humanities · #Bayesian Modeling and Causal Inference #Philosophy and History of Science #Explainable Artificial Intelligence (XAI)
  8. Inherent Trade-Offs in the Fair Determination of Risk Scores
    2017/01/01 by Jon Kleinberg, Sendhil Mullainathan, Manish Raghavan · 1 voice · 35 citations
    Arts and Humanities · Computer Science · #Bayesian Modeling and Causal Inference #Explainable Artificial Intelligence (XAI) #Philosophy and History of Science
  9. Enjoying the Quiet Life? Corporate Governance and Managerial Preferences
    2003/09/23 by Marianne Bertrand, Sendhil Mullainathan · 23 citations
    Business, Management and Accounting · Economics, Econometrics and Finance · #Corporate Finance and Governance #Banking stability, regulation, efficiency #Corporate Insolvency and Governance
  10. Machine Learning: An Applied Econometric Approach
    2017/05/01 by Sendhil Mullainathan, Jann Spiess · 12 citations
    Decision Sciences · Energy · Environmental Science · #Energy, Environment, and Transportation Policies #Forecasting Techniques and Applications #Impact of Light on Environment and Health
  11. Language Generation in the Limit
    2024/04/10 by Jon Kleinberg, Sendhil Mullainathan, Kleinberg, Jon +1 · 4 voices · 12 citations
    #cs.DS #cs.AI #cs.CL #cs.LG
  12. The Algorithmic Automation Problem: Prediction, Triage, and Human Effort
    2019/03/28 by Maithra Raghu, Katy Blumer, Raghu, Maithra +9 · 7 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning in Healthcare
  13. Productivity and Selection of Human Capital with Machine Learning
    2016/05/01 by Aaron Chalfin, Oren Danieli, Andrew Hillis +4 · 11 citations
    Economics, Econometrics and Finance · Social Sciences · #Labor market dynamics and wage inequality #Economic Policies and Impacts #School Choice and Performance
  14. Large Language Models: An Applied Econometric Framework
    2024/12/09 by Jens Ludwig, Sendhil Mullainathan, Ludwig, Jens +3 · 1 voice · 11 citations
    #econ.EM #cs.AI
  15. Making sense of recommendations
    2019/02/14 by Michael Yeomans, Anuj Shah, Sendhil Mullainathan +1 · 5 citations
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence in Games #Recommender Systems and Techniques
  16. The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization
    2022/02/23 by Jon Kleinberg, Kleinberg, Jon, Sendhil Mullainathan +3 · 1 voice · 4 citations
    Computer Science · #Computer Science and Game Theory (cs.GT) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #cs.CY #cs.GT #cs.SI
  17. Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function
    2024/06/03 by Keyon Vafa, Vafa, Keyon, Ashesh Rambachan +3 · 1 voice · 5 citations
    #cs.CL #cs.AI
  18. Direct Uncertainty Prediction for Medical Second Opinions
    2018/07/04 by Maithra Raghu, Katy Blumer, Raghu, Maithra +11 · 3 citations
    Computer Science · Medicine · #Machine Learning in Healthcare #Artificial Intelligence in Healthcare and Education #Clinical Reasoning and Diagnostic Skills
  19. Do Financial Concerns Make Workers Less Productive?
    2024/11/27 by Supreet Kaur, Sendhil Mullainathan, Suanna Oh +1 · 1 voice · 3 citations
    Business, Management and Accounting · #Financial Literacy, Pension, Retirement Analysis
  20. Money in the Mental Lives of the Poor
    2018/02/01 by Anuj K. Shah, Jiaying Zhao, Sendhil Mullainathan +1 · 1 citation
  21. Machine-Learning Tests for Effects on Multiple Outcomes
    2017/07/05 by Jens Ludwig, Ludwig, Jens, Sendhil Mullainathan +3 · 1 citation
    Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
  22. Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and\n Interpretability
    2018/09/12 by Jon Kleinberg, Sendhil Mullainathan, Kleinberg, Jon +1 · 1 citation
    Economics, Econometrics and Finance · Social Sciences · #Computers and Society (cs.CY) #Corruption and Economic Development #Data Structures and Algorithms (cs.DS) #Economic Policies and Impacts #Experimental Behavioral Economics Studies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Social and Information Networks (cs.SI)