Modeling consumer preferences and price sensitivities from large-scale grocery shopping transaction logs M Wan, D Wang, M Goldman, M Taddy, J Rao, J Liu, D Lymberopoulos, ... Proceedings of the 26th international conference on world wide web, 1103-1112, 2017 | 248 | 2017 |
Orthogonal machine learning for demand estimation: High dimensional causal inference in dynamic panels V Chernozhukov, M Goldman, V Semenova, M Taddy arXiv, arXiv: 1712.09988, 2017 | 246* | 2017 |
Comparing distributions by multiple testing across quantiles or CDF values M Goldman, DM Kaplan Journal of Econometrics, 2018 | 115* | 2018 |
Loss aversion around a fixed reference point in highly experienced agents M Goldman, JM Rao Available at SSRN 2782110, 2017 | 83* | 2017 |
Experiments as Instruments: Heterogeneous Position Effects in Sponsored Search Auctions. M Goldman, J Rao EAI Endorsed Trans. Serious Games 3 (11), e2, 2016 | 50 | 2016 |
Optimal stopping in the NBA: Sequential search and the shot clock M Goldman, JM Rao Journal of Economic Behavior & Organization 136, 107-124, 2017 | 46* | 2017 |
Network experimentation at scale B Karrer, L Shi, M Bhole, M Goldman, T Palmer, C Gelman, M Konutgan, ... Proceedings of the 27th acm sigkdd conference on knowledge discovery & data …, 2021 | 38 | 2021 |
goldman M Goldman Gaseous Electronics 1, 219-225, 2012 | 35 | 2012 |
Misperception of Risk and Incentives by Experienced Agents M Goldman, JM Rao | 33* | 2013 |
Optimal strategy in basketball B Skinner, M Goldman Handbook of statistical methods and analyses in sports, 245-260, 2017 | 28 | 2017 |
Fractional order statistic approximation for nonparametric conditional quantile inference M Goldman, DM Kaplan Journal of Econometrics 196 (2), 331-346, 2017 | 27* | 2017 |
Machine learning for variance reduction in online experiments Y Guo, D Coey, M Konutgan, W Li, C Schoener, M Goldman Advances in Neural Information Processing Systems 34, 8637-8648, 2021 | 22 | 2021 |
Non‐parametric inference on (conditional) quantile differences and interquantile ranges, using L‐statistics M Goldman, DM Kaplan The Econometrics Journal 21 (2), 136-169, 2018 | 19* | 2018 |
Estimation and inference on heterogeneous treatment effects in high-dimensional dynamic panels V Semenova, M Goldman, V Chernozhukov, M Taddy | 11 | 2021 |
Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence V Semenova, M Goldman, V Chernozhukov, M Taddy Quantitative Economics 14 (2), 471-510, 2023 | 7 | 2023 |
Regression adjustment with synthetic controls in online experiments C Zhang, D Coey, M Goldman, B Karrer 2021 Conference on Digital Experimentation at MIT, Parallel Session 4B …, 2021 | 3 | 2021 |
Pricing engine: Estimating causal impacts in real world business settings M Goldman, B Quistorff arXiv preprint arXiv:1806.03285, 2018 | 2 | 2018 |
Supplement to ‘Inference on heterogeneous treatment effects in high-dimensional dynamic panels under weak dependence’ V Semenova, M Goldman, V Chernozhukov, M Taddy Quantitative Economics Supplemental Material 14, 2023 | 1 | 2023 |
M. Goldman. Quantum description of high‐resolution NMR in liquids. Oxford University Press, 1988.£ 35.00 M Goldman, GA Webb Magnetic Resonance in Chemistry 27 (5), 507-507, 1989 | 1 | 1989 |
Matching on What Matters: A Pseudo-Metric Learning Approach to Matching Estimation in High Dimensions G Johnson, B Quistorff, M Goldman arXiv preprint arXiv:1905.12020, 2019 | | 2019 |