a growing list of my published works and working papers.

conference papers

  1. Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features
    Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, and Daniel E Ho
    In IEEE Conference on Secure and Trustworthy Machine Learning, Apr 2024
  2. Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
    Nathanael Jo, Sina Aghaei, Andrés Gómez, and Phebe Vayanos
    In AAAI/ACM AI, Ethics, and Society, Aug 2023
  3. Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
    Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice, and Phebe Vayanos
    In AAAI Conference on Artificial Intelligence, Aug 2023

journal articles

  1. Not (Officially) in My Backyard: Characterizing Informal Accessory Dwelling Units and Informing Housing Policy with Remote Sensing
    Nathanael Jo, Andrea Vallebueno, Derek Ouyang, and Daniel E Ho
    Journal of American Planning Association, Jun 2024
  2. Drop a Line, Submit on Time? Randomized Tailored Reminders Improve Pollution Reporting Timeliness
    Elinor Benami, Nathanael Jo, Beth Ragnauth, and Daniel E Ho
    Journal of the Association of Environmental and Resource Economists (forthcoming), Jun 2024

working papers

  1. Learning Optimal Prescriptive Trees from Observational Data
    Nathanael Jo, Sina Aghaei, Andrés Gómez, and Phebe Vayanos
    R&R at Management Science, Jun 2023
  2. ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
    Patrick Vossler, Sina Aghaei, Nathan Justin, Nathanael Jo, Andrés Gómez, and Phebe Vayanos
    Working Paper, Jun 2023