I am an assistant professor of operations, technology and information management at the Johnson Graduate School of Management at Cornell University. My research is at the intersection of causal inference, economics, and operations. I am particularly interested in developing decision-making models that leverage large-scale data combining statistics, optimization, and machine learning.
For a PDF version of my CV, click here
Contact: nur.kaynar@cornell.edu
Research
Working
- Breaking Enrollment Barriers for Refugee Children: Evidence from an Enrollment Support Program in Turkey
with Hanwen Zhu and Yao Cui - Scarcity by Design: How Recommendation Algorithms Impact Prices, Participation, and Customer Welfare [PDF]
with Ömer Sarıtaç and Özge Şahin - Causal Product Networks: A Data-driven Methodology for Modeling Basket-Shopping Consumer Behavior [PDF]
with Ziwei Zhu and Vishal Gaur
Under revision. - Long-Term Policy Prediction with Causal Graphs: Insights from Healthy Grocery Shopping [PDF]
with Dmitry Mitrofanov
Under revision.
Published
- Discovering Causal Models with Optimization: Confounders, Cycles, and Instrument Validity [PDF]
with Auyon Siddiq and Frederick Eberhardt
Management Science, 71(4), 3283-3302, 2024.
- Second Place, ADIA Lab Best Paper Award, 2024 (Prize: $30,000) - Estimating Effects of Incentive Contracts in Online Labor Platforms [PDF]
with Auyon Siddiq
Management Science, 69(4), 2106-2126, 2023. - Data-Driven Decision Support Tools for Water Systems
with Michelle Miro, Aisha Najera Chesler, Kelsea Best, and Rachel Kirpes
Environmental Science and Policy, 393-400, 2021. - Approaches to Analyzing the Vulnerability of Community Water Systems to Groundwater Contamination in Los Angeles County
with Michelle Miro, Aisha Najera Chesler, Kelsea Best, and Rachel Kirpes
Research in Mathematics and Public Policy, Springer, Cham, 19-28, 2020. - Equitable Decision Making Approaches Over Allocations of Multiple Benefits to Multiple Entities
with Özlem Karsu
Omega, 81:85-98, 2017.
Service
- Session co-chair (Causality, Machine Learning, and Optimization, with Auyon Siddiq), INFORMS Annual Meeting, 2021.
- Member of the technical program committee and referee for 20th IEEE International Conference on Machine Learning and Applications (ICMLA, 2021)
- Committee member and referee for 37th Conference on Uncertainty in Artificial Intelligence (UAI, 2021).
- Committee member for 8th Causal Inference Workshop at 37th Conference on Uncertainty in Artificial Intelligence (UAI, 2021).
Conferences and Talks
- Discovering Causal Models with Optimization
- 16th INFORMS Workshop on Data Mining and Decision Analytics, October 2021
- INFORMS Annual Meeting, October 2021
- INFORMS Healthcare Conference, July 2021
- California Institute of Technology, HSS Seminar, July 2021
- Imperial-LBS-UCL ORMS Seminars, June 2021
- MSOM Conference, June 2021
- INFORMS Annual Meeting, November 2020 - Learning Hidden Action Principal-Agent Models
- INFORMS Annual Meeting, October 2019 - A Spatial Model of the Opioid Epidemic in California
- INFORMS Annual Meeting, October 2018 - Handling Multi-Dimensional Efficiency and Equity Concerns
- IMA and OR Society Conference on Mathematics of Operational Research, April 2017
Workshops
- Causality, 2022. Simons Institute for Theory and Computing, University of California, Berkeley (scheduled).
- Women in Mathematics and Public Policy, 2019. Institute for Pure & Applied Mathematics, University of California, Los Angeles.
- Causal Discovery & Causality-Inspired Machine Learning, 2020. Conference on Neural Information Processing Systems (NeurIPS).