| Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. A Hyttinen, F Eberhardt, M Järvisalo UAI, 340-349, 2014 | 162 | 2014 |
| Learning Linear Cyclic Causal Models with Latent Variables A Hyttinen, F Eberhardt, PO Hoyer Journal of Machine Learning Research 13, 3387-3439, 2012 | 152 | 2012 |
| Experiment selection for causal discovery A Hyttinen, F Eberhardt, PO Hoyer The Journal of Machine Learning Research 14 (1), 3041-3071, 2013 | 119 | 2013 |
| Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure A Hyttinen, PO Hoyer, F Eberhardt, M Järvisalo Uncertainty in Artificial Intelligence, 2013 | 119 | 2013 |
| Do-calculus when the True Graph Is Unknown. A Hyttinen, F Eberhardt, M Järvisalo UAI, 395-404, 2015 | 62 | 2015 |
| Causal Discovery from Subsampled Time Series Data by Constraint Optimization A Hyttinen, S Plis, M Järvisalo, F Eberhardt, D Danks International Conference on Probabilistic Graphical Models (PGM), 2016 | 47 | 2016 |
| Bayesian discovery of linear acyclic causal models PO Hoyer, A Hyttinen Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence …, 2009 | 42 | 2009 |
| Identifying causal effects via context-specific independence relations S Tikka, A Hyttinen, J Karvanen Advances in Neural Information Processing Systems 32, NeurIPS 2019., 2020 | 41 | 2020 |
| Towards Scalable Bayesian Learning of Causal DAGs J Viinikka, A Hyttinen, J Pensar, M Koivisto Advances in Neural Information Processing Systems 33, NeurIPS 2020., 2020 | 38 | 2020 |
| Reduced cost fixing in MaxSAT F Bacchus, A Hyttinen, M Järvisalo, P Saikko International Conference on Principles and Practice of Constraint …, 2017 | 38 | 2017 |
| A logical approach to context-specific independence J Corander, A Hyttinen, J Kontinen, J Pensar, J Väänänen Annals of Pure and Applied Logic 170 (9), 975-992, 2019 | 36 | 2019 |
| Applications of MaxSAT in data analysis OJ Berg, AJ Hyttinen, MJ Järvisalo International Conferences on Theory and Applications of Satisfiability …, 2019 | 35 | 2019 |
| Causal effect identification from multiple incomplete data sources: A general search-based approach S Tikka, A Hyttinen, J Karvanen Journal of Statistical Software 99 (5), 2021 | 32 | 2021 |
| Learning Optimal Chain Graphs with Answer Set Programming D Sonntag, M Järvisalo, JM Pena, A Hyttinen http://auai.org/uai2015/proceedings/papers/189.pdf, 2015 | 26 | 2015 |
| Causal discovery for linear cyclic models with latent variables A Hyttinen, F Eberhardt, PO Hoyer Fifth European Workshop on Probabilistic Graphical Models (PGM-2010), 2010 | 25* | 2010 |
| A constraint optimization approach to causal discovery from subsampled time series data A Hyttinen, S Plis, M Järvisalo, F Eberhardt, D Danks International Journal of Approximate Reasoning 90, 208-225, 2017 | 23 | 2017 |
| A Core-Guided Approach to Learning Optimal Causal Graphs. A Hyttinen, P Saikko, M Järvisalo IJCAI, 645-651, 2017 | 19 | 2017 |
| Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables A Hyttinen, F Eberhardt, PO Hoyer Uncertainty in Artificial Intelligence, 2012 | 19 | 2012 |
| Do-search: A tool for causal inference and study design with multiple data sources J Karvanen, S Tikka, A Hyttinen Epidemiology 32 (1), 111-119, 2021 | 17 | 2021 |
| Discovering causal graphs with cycles and latent confounders: An exact branch-and-bound approach K Rantanen, A Hyttinen, M Järvisalo International Journal of Approximate Reasoning 117, 29-49, 2020 | 17 | 2020 |
Patrik HoyerAlumni, Aalto University and University of HelsinkiVerified email at alumni.aalto.fi
