Matt Goldman
Matt Goldman
Researcher at Microsoft Research
Verified email at - Homepage
Cited by
Cited by
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
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
Comparing distributions by multiple testing across quantiles or CDF values
M Goldman, DM Kaplan
Journal of Econometrics, 2018
Loss aversion around a fixed reference point in highly experienced agents
M Goldman, JM Rao
Available at SSRN 2782110, 2017
Experiments as Instruments: Heterogeneous Position Effects in Sponsored Search Auctions.
M Goldman, J Rao
EAI Endorsed Trans. Serious Games 3 (11), e2, 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
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
M Goldman
Gaseous Electronics 1, 219-225, 2012
Misperception of Risk and Incentives by Experienced Agents
M Goldman, JM Rao
Optimal strategy in basketball
B Skinner, M Goldman
Handbook of statistical methods and analyses in sports, 245-260, 2017
Fractional order statistic approximation for nonparametric conditional quantile inference
M Goldman, DM Kaplan
Journal of Econometrics 196 (2), 331-346, 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
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
Estimation and inference on heterogeneous treatment effects in high-dimensional dynamic panels
V Semenova, M Goldman, V Chernozhukov, M Taddy
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
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
Pricing engine: Estimating causal impacts in real world business settings
M Goldman, B Quistorff
arXiv preprint arXiv:1806.03285, 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
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
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
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