A sound and complete algorithm for learning causal models from relational data M Maier, K Marazopoulou, D Arbour, D Jensen arXiv preprint arXiv:1309.6843, 2013 | 59 | 2013 |
Time-uniform central limit theory, asymptotic confidence sequences, and anytime-valid causal inference I Waudby-Smith, D Arbour, R Sinha, EH Kennedy, A Ramdas arXiv preprint arXiv:2103.06476 11, 2021 | 44* | 2021 |
Inferring Network Effects from Observational Data D Arbour, D Garant, D Jensen KDD, 2016 | 39 | 2016 |
Heterogeneous network motifs RA Rossi, NK Ahmed, A Carranza, D Arbour, A Rao, S Kim, E Koh arXiv preprint arXiv:1901.10026, 2019 | 35 | 2019 |
Permutation weighting D Arbour, D Dimmery, A Sondhi International Conference on Machine Learning, 331-341, 2021 | 27 | 2021 |
Heterogeneous graphlets RA Rossi, NK Ahmed, A Carranza, D Arbour, A Rao, S Kim, E Koh ACM Transactions on Knowledge Discovery from Data (TKDD) 15 (1), 1-43, 2020 | 21 | 2020 |
Balanced off-policy evaluation in general action spaces A Sondhi, D Arbour, D Dimmery International Conference on Artificial Intelligence and Statistics, 2413-2423, 2020 | 20 | 2020 |
Adjusting for confounders with text: Challenges and an empirical evaluation framework for causal inference G Weld, P West, M Glenski, D Arbour, RA Rossi, T Althoff Proceedings of the international AAAI conference on web and social media 16 …, 2022 | 19 | 2022 |
Anytime-valid confidence sequences in an enterprise a/b testing platform A Maharaj, R Sinha, D Arbour, I Waudby-Smith, SZ Liu, M Sinha, ... Companion Proceedings of the ACM Web Conference 2023, 396-400, 2023 | 15 | 2023 |
Estimating the effects of a California gun control program with multitask Gaussian processes E Ben-Michael, D Arbour, A Feller, A Franks, S Raphael The Annals of Applied Statistics 17 (2), 985-1016, 2023 | 14* | 2023 |
General identification of dynamic treatment regimes under interference E Sherman, D Arbour, I Shpitser International Conference on Artificial Intelligence and Statistics, 3917-3927, 2020 | 13 | 2020 |
Propensity Score Matching for Causal Inference with Relational Data. DT Arbour, K Marazopoulou, D Garant, DD Jensen CI@ UAI, 25-34, 2014 | 12 | 2014 |
First experiences with a classroom recording system PE Dickson, WR Adrion, AR Hanson, DT Arbour Proceedings of the 14th annual ACM SIGCSE conference on Innovation and …, 2009 | 12 | 2009 |
Causal inference from network data E Zheleva, D Arbour Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 10 | 2021 |
Efficient balanced treatment assignments for experimentation D Arbour, D Dimmery, A Rao International Conference on Artificial Intelligence and Statistics, 3070-3078, 2021 | 10 | 2021 |
Evaluation of automatic classroom capture for computer science education PE Dickson, DT Arbour, WR Adrion, A Gentzel Proceedings of the fifteenth annual conference on Innovation and technology …, 2010 | 9 | 2010 |
Generating and controlling diversity in image search MM Tanjim, R Sinha, KK Singh, S Mahadevan, D Arbour, M Sinha, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 8 | 2022 |
Sample constrained treatment effect estimation R Addanki, D Arbour, T Mai, C Musco, A Rao Advances in Neural Information Processing Systems 35, 5417-5430, 2022 | 7 | 2022 |
Constraint sampling reinforcement learning: Incorporating expertise for faster learning T Mu, G Theocharous, D Arbour, E Brunskill Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7841-7849, 2022 | 7 | 2022 |
Inferring Causal Direction from Relational Data D Arbour, K Marazopoulou, D Jensen Uncertainty in Artificial Intelligence, 2016 | 7 | 2016 |