Fetching the paper…
Reading the bibliography…
Evaluation of intervention in a multiagent system, e.g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields.
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence , 2019, pp. 1907–1913
1913
Earlier work this paper cites.
D. B. Rubin, “Bayesian inference for causal effects: The role of randomization,” The Annals of statistics , pp. 34–58, 1978
1978
Earlier work this paper cites.
P. R. Rosenbaum and D. B. Rubin, “The central role of the propensity score in observational studies for causal effects,” Biometrika , vol. 70, no. 1, pp. 41–55, 1983
1983
Earlier work this paper cites.
J. Robins, “A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect,” Mathematical Modelling , vol. 7, no. 9-12, pp. 1393–1512, 1986
1986
Earlier work this paper cites.
C. W. Reynolds, “Flocks, herds and schools: A distributed behavioral model,” in Proceedings of the 14th annual Conference on Computer Graphics and Interactive Techniques , 1987, pp. 25–34
1987
Earlier work this paper cites.
J. M. Robins, “Correcting for non-compliance in randomized trials using structural nested mean models,” Communications in Statistics-Theory and methods , vol. 23, no. 8, pp. 2379–2412, 1994
1994
Earlier work this paper cites.
T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, and O. Shochet, “Novel type of phase transition in a system of self-driven particles,” Physical Review Letters , vol. 75, no. 6, pp. 1226–1229, 1995
1995
Earlier work this paper cites.
J. M. Robins, M. A. Hernan, and B. Brumback, “Marginal structural models and causal inference in epidemiology,” 2000
2000
Earlier work this paper cites.
I. D. Couzin, J. Krause, R. James, G. D. Ruxton, and N. R. Franks, “Collective memory and spatial sorting in animal groups,” Journal of Theoretical Biology , vol. 218, no. 1, pp. 1–11, 2002
2002
Earlier work this paper cites.
C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance,” Climate Research , vol. 30, no. 1, pp. 79–82, 2005
2005
Earlier work this paper cites.
J. M. Robins and M. A. Hernán, “Estimation of the causal effects of time-varying exposures,” in Longitudinal Data Analysis , G. Fitzmaurice, M. Davidian, G. Verbeke et al. , Eds. New York, NY: Chapman & Hall/CRC Press, 2009, pp. 553–597
2009
Earlier work this paper cites.
J. Pearl, Causality . Cambridge university press, 2009
2009
Earlier work this paper cites.
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y. Ng et al. , “Ros: an open-source robot operating system,” in ICRA workshop on open source software , vol. 3, no. 3.2. Kobe, Japan, 2009, p. 5
2009
Earlier work this paper cites.
M. A. Hernán and J. M. Robins, Causal inference . CRC Boca Raton, FL, 2010
2010
Earlier work this paper cites.
J. L. Hill, “Bayesian nonparametric modeling for causal inference,” Journal of Computational and Graphical Statistics , vol. 20, no. 1, pp. 217–240, 2011
2011
Earlier work this paper cites.
T. A. Glass, S. N. Goodman, M. A. Hernán, and J. M. Samet, “Causal inference in public health,” Annual Review of Public Health , vol. 34, pp. 61–75, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
N. Baum-Snow and F. Ferreira, “Causal inference in urban and regional economics,” in Handbook of regional and urban economics . Elsevier, 2015, vol. 5, pp. 3–68
2015
Earlier work this paper cites.
P. Wang, W. Sun, D. Yin, J. Yang, and Y. Chang, “Robust tree-based causal inference for complex ad effectiveness analysis,” in Proceedings of the Eighth ACM International Conference on Web Search and Data Mining , 2015, pp. 67–76
2015
Earlier work this paper cites.
J. Chung, K. Kastner, L. Dinh, K. Goel, A. C. Courville, and Y. Bengio, “A recurrent latent variable model for sequential data,” in Advances in Neural Information Processing Systems 28 , 2015, pp. 2980–2988
2015
Earlier work this paper cites.
S. Kato, E. Takeuchi, Y. Ishiguro, Y. Ninomiya, K. Takeda, and T. Hamada, “An open approach to autonomous vehicles,” IEEE Micro , vol. 35, no. 6, pp. 60–68, 2015
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
M. Fraccaro, S. K. Sønderby, U. Paquet, and O. Winther, “Sequential neural models with stochastic layers,” in Advances in Neural Information Processing Systems 29 , 2016, pp. 2199–2207
2016
Earlier work this paper cites.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 2096–2030, 2016
2016
Earlier work this paper cites.
Y. Xu, Y. Xu, and S. Saria, “A bayesian nonparametric approach for estimating individualized treatment-response curves,” in Machine Learning for Healthcare Conference . PMLR, 2016, pp. 282–300
2016
Earlier work this paper cites.
F. Johansson, U. Shalit, and D. Sontag, “Learning representations for counterfactual inference,” in International Conference on Machine Learning . PMLR, 2016, pp. 3020–3029
2016
Earlier work this paper cites.
A. Lerer, S. Gross, and R. Fergus, “Learning physical intuition of block towers by example,” in International conference on machine learning . PMLR, 2016, pp. 430–438
2016
Earlier work this paper cites.
K. Fujii, K. Yokoyama, T. Koyama, A. Rikukawa, H. Yamada, and Y. Yamamoto, “Resilient help to switch and overlap hierarchical subsystems in a small human group,” Scientific Reports , vol. 6, 2016
2016
Earlier work this paper cites.
A. G. A. P. Goyal, A. Sordoni, M.-A. Côté, N. R. Ke, and Y. Bengio, “Z-forcing: Training stochastic recurrent networks,” in Advances in Neural Information Processing Systems 30 , 2017, pp. 6713–6723
2017
Cited alongside, same era.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, “Deep sets,” Advances in Neural Information Processing Systems , vol. 30, pp. 3394––3404, 2017
2017
Cited alongside, same era.
U. Shalit, F. D. Johansson, and D. Sontag, “Estimating individual treatment effect: generalization bounds and algorithms,” in International Conference on Machine Learning . PMLR, 2017, pp. 3076–3085
2017
Cited alongside, same era.
P. Schulam and S. Saria, “Reliable decision support using counterfactual models,” Advances in Neural Information Processing Systems , vol. 30, pp. 1697–1708, 2017
2017
Cited alongside, same era.
R. Liu, C. Yin, and P. Zhang, “Estimating individual treatment effects with time-varying confounders,” in 2020 IEEE International Conference on Data Mining (ICDM) . IEEE, 2020, pp. 382–391
2020
Later among the works it cites.
K. Fujii, N. Takeishi, M. Hojo, Y. Inaba, and Y. Kawahara, “Physically-interpretable classification of network dynamics for complex collective motions,” Scientific Reports , vol. 10, no. 3005, 2020
2020
Later among the works it cites.
Q. Zhang, J. Chang, G. Meng, S. Xiang, and C. Pan, “Spatio-temporal graph structure learning for traffic forecasting,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 01, 2020, pp. 1177–1185
2020
Later among the works it cites.
G. Liu, Y. Luo, O. Schulte, and T. Kharrat, “Deep soccer analytics: learning an action-value function for evaluating soccer players,” Data Mining and Knowledge Discovery , vol. 34, no. 5, pp. 1531–1559, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Soleimani, A. Subbaswamy, and S. Saria, “Treatment-response models for counterfactual reasoning with continuous-time, continuous-valued interventions,” in 33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017 . AUAI Press Corvallis, OR, 2017
2017
Cited alongside, same era.
J. Roy, K. J. Lum, and M. J. Daniels, “A bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome,” Biostatistics , vol. 18, no. 1, pp. 32–47, 2017
2017
Cited alongside, same era.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on Robot Learning . PMLR, 2017, pp. 1–16
2017
Cited alongside, same era.
K. Fujii, Y. Inaba, and Y. Kawahara, “Koopman spectral kernels for comparing complex dynamics: Application to multiagent sport plays,” in European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD’17) . NY: Springer, 2017, pp. 127–139
2017
Cited alongside, same era.
2017
Cited alongside, same era.
B. Lim, A. M. Alaa, and M. van der Schaar, “Forecasting treatment responses over time using recurrent marginal structural networks.” Advances in Neural Information Processing Systems , vol. 18, pp. 7483–7493, 2018
2018
Cited alongside, same era.
K. Fujii, T. Kawasaki, Y. Inaba, and Y. Kawahara, “Prediction and classification in equation-free collective motion dynamics,” PLoS Computational Biology , vol. 14, no. 11, p. e1006545, 2018
2018
Cited alongside, same era.
T. Kipf, E. Fetaya, K.-C. Wang, M. Welling, and R. Zemel, “Neural relational inference for interacting systems,” in International Conference on Machine Learning , 2018, pp. 2688–2697
2018
Cited alongside, same era.
T. You and B. Han, “Traffic accident benchmark for causality recognition,” in European Conference on Computer Vision . NY: Springer, 2020, pp. 540–556
2020
Later among the works it cites.
2020
Later among the works it cites.
J. Ma, R. Guo, C. Chen, A. Zhang, and J. Li, “Deconfounding with networked observational data in a dynamic environment,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining , 2021, pp. 166–174
2021
Later among the works it cites.
N. Takeishi and A. Kalousis, “Physics-integrated variational autoencoders for robust and interpretable generative modeling,” Advances in Neural Information Processing Systems , vol. 34, pp. 14 809–14 821, 2021
2021
Later among the works it cites.
Q. Li, Z. Wang, S. Liu, G. Li, and G. Xu, “Causal optimal transport for treatment effect estimation,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–13, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
K. Fujii, N. Takeishi, K. Tsutsui, E. Fujioka, N. Nishiumi, R. Tanaka, M. Fukushiro, K. Ide, H. Kohno, K. Yoda, S. Takahashi, S. Hiryu, and Y. Kawahara, “Learning interaction rules from multi-animal trajectories via augmented behavioral models,” Advances in Neural Information Processing Systems , vol. 34, pp. 11 108–11 122, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
K. Takeuchi, R. Nishida, H. Kashima, and M. Onishi, “Grab the reins of crowds: Estimating the effects of crowd movement guidance using causal inference,” in Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems , 2021, pp. 1290–1298
2021
Later among the works it cites.
K. Fujii, K. Takeuchi, A. Kuribayashi, N. Takeishi, Y. Kawahara, and K. Takeda, “Estimating counterfactual treatment outcomes over time in multi-vehicle simulation,” in Proceedings of the 30th International Conference on Advances in Geographic Information Systems (SIGSPATIAL’22) , 2022
2022
Closest in time.
P. Grecov, A. N. Prasanna, K. Ackermann, S. Campbell, D. Scott, D. I. Lubman, and C. Bergmeir, “Probabilistic causal effect estimation with global neural network forecasting models,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–15, July 2022
2022
Closest in time.
M. Abroshan, K. H. Yip, C. Tekin, and M. van der Schaar, “Conservative policy construction using variational autoencoders for logged data with missing values,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–11, 2022
2022
Closest in time.
N. Seedat, F. Imrie, A. Bellot, Z. Qian, and M. van der Schaar, “Continuous-time modeling of counterfactual outcomes using neural controlled differential equations,” in International Conference on Machine Learning . PMLR, 2022, pp. 19 497–19 521
2022
Closest in time.
H. Nakahara, K. Takeda, and K. Fujii, “Estimating the effect of hitting strategies in baseball using counterfactual virtual simulation with deep learning,” International Journal of Computer Science in Sport , vol. 22, no. 1, pp. 1–12, January 2022
2022
Closest in time.
M. Teranishi, K. Tsutsui, K. Takeda, and K. Fujii, “Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction,” in International Workshop on Machine Learning and Data Mining for Sports Analytics . NY: Springer, 2022, pp. 53–73
2022
Closest in time.
P. Rahimian, J. Van Haaren, T. Abzhanova, and L. Toka, “Beyond action valuation: A deep reinforcement learning framework for optimizing player decisions in soccer,” in 16th Annual MIT Sloan Sports Analytics Conference. Boston, MA, USA: MIT , 2022, p. 25
2022
Closest in time.
K. Toda, M. Teranishi, K. Kushiro, and K. Fujii, “Evaluation of soccer team defense based on prediction models of ball recovery and being attacked,” PLoS One , vol. 17, no. 1, p. e0263051, 2022
2022
Closest in time.
K. Takeuchi, R. Nishida, H. Kashima, and M. Onishi, “Causal effect estimation on hierarchical spatial graph data,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2023, pp. 2145–2154
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
H. Nakahara, K. Tsutsui, K. Takeda, and K. Fujii, “Action valuation of on-and off-ball soccer players based on multi-agent deep reinforcement learning,” IEEE Access , vol. 11, pp. 131 237–131 244, 2023
2023
Closest in time.
H. Nakahara, K. Takeda, and K. Fujii, “Pitching strategy evaluation via stratified analysis using propensity score,” Journal of Quantitative Analysis in Sports , vol. 19, no. 2, pp. 91–102, 2023
2023
Closest in time.
K. Fujii, N. Takeishi, Y. Kawahara, and K. Takeda, “Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling,” Neural Networks , vol. 171, pp. 40–52, 2024
2024
Closest in time.