Ariel Boyarsky

Ph.D

Operations Research

Columbia University - Columbia Business School

PhD Candidate

- Published "Modeling Interference with Experiment Roll-out" in Proceedings of ACM Economics & Com- putation 2023. Presented at INFORMS 2022/2023 and CoDE 2022. Journal version under review. • Developed model selection algorithm using roll-out designs to estimate treatment e ects under interference. • Proved roll-out designs increase the likelihood of identifying causal effects and quantified variance reduction. - "External Validity of A/B Testing under Effect Ordering Principle." Conference version under review. • Assess external validity of A/B tests under effect-ordering assumption using mixed integer programming. • Prove asymptotic coverage guarantees for causal effects estimated using an MIP algorithm with DNN inputs. - Courses taught: Stochastic Models (PhD), Foundations of AI for Business (MBA/MS), Managerial Stats (MBA).