Mullainathan, Sendhil

Money in the Mental Lives of the Poor

Shah, A. K., Zhao, J., Mullainathan, S., & Shafir, E. (2018) · Social Cognition

Decision-Making and Behavioral EconomicsInnovation and Socioeconomic Development

Cited by 181

Does Machine Learning Automate Moral Hazard and Error?

Mullainathan, S., & Obermeyer, Z. (2017) · American Economic Review

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 156

The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables

Lakkaraju, H., Kleinberg, J., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Advanced Causal Inference TechniquesEthics and Social Impacts of AI

Cited by 138

Thinking, fast and slow? Some field experiments to reduce crime and dropout in chicago

Heller, S., Shah, A., Guryan, J., Ludwig, J., Mullainathan, S., & Pollack, H. (2015) · National Bureau of Economic Research

Crime Patterns and InterventionsCriminal Justice and Corrections Analysis

Cited by 137

Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People

Obermeyer, Z., & Mullainathan, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 124

Do Firm Boundaries Matter?

Mullainathan, S., & Scharfstein, D. (2001) · American Economic Review

Merger and Competition AnalysisOutsourcing and Supply Chain Management

Cited by 96

Direct Uncertainty Prediction for Medical Second Opinions

Raghu, M., Blumer, K., Sayres, R., Obermeyer, Z., Kleinberg, R., Mullainathan, S., & Kleinberg, J. (2018) · arXiv

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 93

Scarcity and Cognitive Function around Payday: A Conceptual and Empirical Analysis

Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2020) · Journal of the Association for Consumer Research

Decision-Making and Behavioral EconomicsFinancial Literacy, Pension, Retirement Analysis

Cited by 73

An exercise in self-replication: Replicating Shah, Mullainathan, and Shafir (2012)

Shah, A. K., Mullainathan, S., & Shafir, E. (2019) · Journal of Economic Psychology

Decision-Making and Behavioral EconomicsExperimental Behavioral Economics Studies

Cited by 72

The Algorithmic Automation Problem: Prediction, Triage, and Human Effort

Raghu, M., Blumer, K., Corrado, G., Kleinberg, J., Obermeyer, Z., & Mullainathan, S. (2019) · arXiv

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 58

Do Cigarette Taxes Make Smokers Happier

Gruber, J. H., & Mullainathan, S. (2005) · The B.E. Journal of Economic Analysis & Policy

Smoking Behavior and CessationEconomic and Fiscal Studies

Cited by 56

Solving medicine's data bottleneck: Nightingale Open Science

Mullainathan, S., & Obermeyer, Z. (2022) · Nature Medicine

Research Data Management PracticesScientific Computing and Data Management

Cited by 43

Allocation of COVID-19 Relief Funding to Disproportionately Black Counties

Kakani, P., Chandra, A., Mullainathan, S., & Obermeyer, Z. (2020) · JAMA

COVID-19 Clinical Research StudiesCOVID-19 epidemiological studies

Cited by 41

Response to Comment on “Poverty Impedes Cognitive Function”

Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013) · Science

Income, Poverty, and InequalityDecision-Making and Behavioral Economics

Cited by 35

The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

Kleinberg, J., Mullainathan, S., & Raghavan, M. (2023) · Management Science

Recommender Systems and TechniquesAdvanced Bandit Algorithms Research

Cited by 34

Automating Automaticity: How the Context of Human Choice Affects the Extent of Algorithmic Bias

Agan, A., Davenport, D., Ludwig, J., & Mullainathan, S. (2023) · National Bureau of Economic Research

Ethics and Social Impacts of AINames, Identity, and Discrimination Research

Cited by 25

Evaluating the World Model Implicit in a Generative Model

Vafa, K., Chen, J. Y., Rambachan, A., Kleinberg, J., & Mullainathan, S. (2024) · arXiv

Reinforcement Learning in RoboticsBayesian Modeling and Causal Inference

Cited by 22

Evaluating the World Model Implicit in a Generative Model

Vafa, K., Chen, J., Rambachan, A., Kleinberg, J., & Mullainathan, S. (2024) · Advances in Neural Information Processing Systems 37

Reinforcement Learning in RoboticsBayesian Modeling and Causal Inference

Cited by 22

The impact of electoral debate on public opinions: an experimental investigation of the 2005 New York City mayoral election

Mullainathan, S., Washington, E., & Azari, J. R. (2010) · Political Representation

Electoral Systems and Political ParticipationMedia Influence and Politics

Cited by 15

What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models

Vafa, K., Chang, P. G., Rambachan, A., & Mullainathan, S. (2025) · arXiv

Domain Adaptation and Few-Shot LearningReinforcement Learning in Robotics

Cited by 3