| Close enough? a large-scale exploration of non-experimental approaches to advertising measurement BR Gordon, R Moakler, F Zettelmeyer Marketing Science 42 (4), 768-793, 2023 | 77 | 2023 |
| Enhancing transparency and control when drawing data-driven inferences about individuals D Chen, SP Fraiberger, R Moakler, F Provost Big data 5 (3), 197-212, 2017 | 65 | 2017 |
| Measuring causal impact of online actions via natural experiments: Application to display advertising DN Hill, R Moakler, AE Hubbard, V Tsemekhman, F Provost, ... Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 34 | 2015 |
| Estimating the value of offsite data to advertisers on meta N Wernerfelt, A Tuchman, B Shapiro, R Moakler University of Chicago, Becker Friedman Institute for Economics Working Paper, 2022 | 33 | 2022 |
| Predictive Incrementality by Experimentation (PIE) for Ad Measurement BR Gordon, R Moakler, F Zettelmeyer arXiv preprint arXiv:2304.06828, 2023 | 5 | 2023 |
| Close enough BR Gordon, R Moakler, F Zettelmeyer A Large-Scale Exploration of Non-Experimental Approaches to Advertising …, 2022 | 5 | 2022 |
| Estimating the value of offsite tracking data to advertisers: Evidence from meta N Wernerfelt, A Tuchman, BT Shapiro, R Moakler Marketing Science, 2024 | 1 | 2024 |
| Generation and delivery of content via remote rendering and data streaming R Ravuru, B Fox, KC Pridmore, MA Perez, X Zhou, AT Olojede, KS Shank, ... US Patent 11,647,238, 2023 | 1 | 2023 |
| Methods for Causal Inference Using Large-scale Digital Data R Moakler New York University, Graduate School of Business Administration, 2017 | | 2017 |
| A framework for improving advertising creative using digital measurement L Andrews, A Olojede, R Moakler, N Nawathe, M Zhou | | |
Foster ProvostProfessor, New York Universitystern.nyu.edu의 이메일 확인됨
