| Text as data M Gentzkow, B Kelly, M Taddy Journal of Economic Literature 57 (3), 535-574, 2019 | 1238 | 2019 |
| Measuring polarization in high-dimensional data: Method and application to congressional speech M Gentzkow, J Shapiro, M Taddy eSocialSciences Working Papers, 2016 | 523* | 2016 |
| Deep IV: A flexible approach for counterfactual prediction J Hartford, G Lewis, K Leyton-Brown, M Taddy International Conference on Machine Learning, 1414-1423, 2017 | 521* | 2017 |
| The geometry of culture: Analyzing the meanings of class through word embeddings AC Kozlowski, M Taddy, JA Evans American Sociological Review 84 (5), 905-949, 2019 | 472 | 2019 |
| On estimation and selection for topic models M Taddy Artificial intelligence and statistics, 1184-1193, 2012 | 309 | 2012 |
| Multinomial inverse regression for text analysis M Taddy Journal of the American Statistical Association 108 (503), 755-770, 2013 | 282 | 2013 |
| Measuring technological innovation over the long run B Kelly, D Papanikolaou, A Seru, M Taddy American Economic Review: Insights 3 (3), 303-320, 2021 | 274 | 2021 |
| 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 | 228 | 2017 |
| Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels V Semenova, M Goldman, V Chernozhukov, M Taddy arXiv preprint arXiv:1712.09988, 2018 | 199* | 2018 |
| A Bayesian nonparametric approach to inference for quantile regression MA Taddy, A Kottas Journal of Business & Economic Statistics 28 (3), 357-369, 2010 | 146 | 2010 |
| Dynamic trees for learning and design MA Taddy, RB Gramacy, NG Polson Journal of the American Statistical Association 106 (493), 109-123, 2011 | 142 | 2011 |
| A nonparametric bayesian analysis of heterogenous treatment effects in digital experimentation M Taddy, M Gardner, L Chen, D Draper Journal of Business & Economic Statistics 34 (4), 661-672, 2016 | 124 | 2016 |
| The technological elements of artificial intelligence M Taddy The economics of artificial intelligence: An agenda, 61-87, 2018 | 118 | 2018 |
| Distributed multinomial regression M Taddy arXiv preprint arXiv:1311.6139, 2015 | 103 | 2015 |
| Bayesian guided pattern search for robust local optimization MA Taddy, HKH Lee, GA Gray, JD Griffin Technometrics 51 (4), 389-401, 2009 | 103 | 2009 |
| Autoregressive mixture models for dynamic spatial Poisson processes: Application to tracking intensity of violent crime MA Taddy Journal of the American Statistical Association 105 (492), 1403-1417, 2010 | 101 | 2010 |
| Business data science: Combining machine learning and economics to optimize, automate, and accelerate business decisions M Taddy McGraw-Hill Education, 2019 | 91 | 2019 |
| Estimating player contribution in hockey with regularized logistic regression RB Gramacy, ST Jensen, M Taddy Journal of Quantitative Analysis in Sports 9 (1), 97-111, 2013 | 90 | 2013 |
| Particle learning for general mixtures CM Carvalho, HF Lopes, NG Polson, MA Taddy | 88 | 2010 |
| Document classification by inversion of distributed language representations M Taddy arXiv preprint arXiv:1504.07295, 2015 | 81 | 2015 |
Robert B. GramacyVirginia TechVerified email at vt.edu
