| 2025 | ICML | Double Machine Learning for Causal Inference under Shared-State Interference. | Chris Hays, Manish Raghavan |
| 2024 | WWW | Content Moderation and the Formation of Online Communities: A Theoretical Framework. | Cynthia Dwork, Chris Hays, Jon M. Kleinberg, Manish Raghavan |
| 2024 | WWW | Reconciling the Accuracy-Diversity Trade-off in Recommendations. | Kenny Peng, Manish Raghavan, Emma Pierson, Jon M. Kleinberg, Nikhil Garg |
| 2023 | WWW | Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection. | Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, Philipp Zimmer |
| 2021 | UAI | Stochastic model for sunk cost bias. | Jon M. Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar |
| 2020 | AAAI | Designing Evaluation Rules That Are Robust to Strategic Behavior. | Jon M. Kleinberg, Manish Raghavan |
| 2019 | EC | How Do Classifiers Induce Agents to Invest Effort Strategically? | Jon M. Kleinberg, Manish Raghavan |
| 2019 | ICML | Hiring Under Uncertainty. | Manish Purohit, Sreenivas Gollapudi, Manish Raghavan |
| 2018 | COLT | The Externalities of Exploration and How Data Diversity Helps Exploitation. | Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu |
| 2018 | WWW | Mapping the Invocation Structure of Online Political Interaction. | Manish Raghavan, Ashton Anderson, Jon M. Kleinberg |
| 2014 | WWW | Deduplicating a places database. | Nilesh N. Dalvi, Marian Olteanu, Manish Raghavan, Philip Bohannon |