Causality: from data to science
Inaugural speech, delivered on October 13, 2022
[3585 KB pdf] [BibTeX] [abstract] [video recording]
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Robustness of Model Predictions under Extension
Causal Bandits without Prior Knowledge using Separating Sets
Local Constraint-Based Causal Discovery under Selection Bias
2021:
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Foundations of Structural Causal Models with Cycles and Latent Variables[960 KB pdf (main paper + supplement)] [484 KB pdf (main paper)] [589 KB pdf (supplement)] [BibTeX] [abstract] [arXiv:1611.06221v6] [DOI]
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Conditional Independences and Causal Relations implied by Sets of Equations
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A Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery
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A Weaker Faithfulness Assumption based on Triple Interactions
2020:
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Constraint-Based Causal Discovery with Partial Ancestral Graphs in the presence of Cycles
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Joint Causal Inference from Multiple Contexts
2019:
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Boosting Local Causal Discovery in High-Dimensional Expression Data2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)
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Beyond Structural Causal Models: Causal Constraints Models
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Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
2018:
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Domain Adaptation by Using Causal Inference to Predict Invariant Conditional DistributionsAdvances in Neural Information Processing Systems 31 (NeurIPS 2018)
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An Upper Bound for Random Measurement Error in Causal DiscoveryProceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2018)
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Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent ConfoundersProceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2018)(The code published in the paper contains multiple bugs: one of the rules for sigma-separation is missing, and some of the conditioning rules were incorrectly implemented (the rules in Def. 2.18 and 2.19 in the paper should be correct). This has been fixed in the code in the github repository. The empirical results in the paper are hardly affected and the conclusions are unaltered.)
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From Deterministic ODEs to Dynamic Structural Causal ModelsProceedings of the 34th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2018)
2017:
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Algebraic Equivalence of Linear Structural Equation ModelsProceedings of the 33rd Annual Conference on Uncertainty in Artificial Intelligence (UAI-17) (UAI 2017)
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Causal Effect Inference with Deep Latent-Variable ModelsAdvances in Neural Information Processing Systems 30 (NIPS*2017)
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Causal Consistency of Structural Equation ModelsProceedings of the 33rd Annual Conference on Uncertainty in Artificial Intelligence (UAI-17) (UAI 2017)
2016:
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Ancestral Causal InferenceAdvances in Neural Information Processing Systems 29 (NIPS*2016)
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Methods for causal inference from gene perturbation experiments and validation
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Distinguishing cause from effect using observational data: methods and benchmarks
2015:
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An Empirical Study of one of the Simplest Causal Prediction AlgorithmsProceedings of the UAI 2015 Workshop on Advances in Causal Inference (UAI2015CI)
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MAGMA: Generalized Gene-Set Analysis of GWAS Data
2014:
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Causal Discovery with Continuous Additive Noise Models
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Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative ExampleProceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction (UAI2014CI)
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Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction[1111 KB pdf] [BibTeX]
2013:
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Cyclic Causal Discovery from Continuous Equilibrium DataProceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI-13) (UAI 2013)
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From Ordinary Differential Equations to Structural Causal Models: the deterministic caseProceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI-13) (UAI 2013)
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Learning Sparse Causal Models is not NP-HardProceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI-13) (UAI 2013)
2012:
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On causal and anticausal learningProceedings of the 29th Annual International Conference on Machine Learning (ICML 2012)
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Information-geometric approach to inferring causal directions
2011:
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On Causal Discovery with Cyclic Additive Noise ModelsAdvances in Neural Information Processing Systems 24 (NIPS*2011)
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Efficient inference in matrix-variate Gaussian models with iid observation noiseAdvances in Neural Information Processing Systems 24 (NIPS*2011)
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Learning of causal relationsProceedings of the 19th European Symposium on Artificial Neural Networks (ESANN 2011)
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Identifiability of Causal Graphs using Functional ModelsProceedings of the 27th Annual Conference on Uncertainty in Artificial Intelligence (UAI-11) (UAI 2011)
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A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller
2010:
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Probabilistic latent variable models for distinguishing between cause and effectAdvances in Neural Information Processing Systems 23 (NIPS*2010)
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libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical ModelsSee also the libDAI webpage.
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Inferring deterministic causal relationsProceedings of the 26th Annual Conference on Uncertainty in Artificial Intelligence (UAI-10) (UAI 2010)(Best student paper award)
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Remote Sensing Feature Selection by Kernel Dependence MeasuresIEEE Geoscience and Remote Sensing Letters 7(3):587-591, July 2010(IEEE Geoscience and Remote Sensing Society 2011 Letters Prize Paper Award)
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Distinguishing between cause and effectJournal of Machine Learning Research Workshop & Conference Proceedings 6(Feb):147-156, 2010
2009:
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Identifying confounders using additive noise modelsProceedings of the 25th Annual Conference on Uncertainty in Artificial Intelligence (UAI-09) (UAI 2009)
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Regression by dependence minimization and its application to causal inferenceProceedings of the 26th Annual International Conference on Machine Learning (ICML 2009) (ICML 2009)
2008:
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Nonlinear causal discovery with additive noise modelsAdvances in Neural Information Processing Systems 21 (NIPS*2008)(Corollary 2 which appeared in a previous version of this paper has been removed because it contained an error)
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Bounds on marginal probability distributionsAdvances in Neural Information Processing Systems 21 (NIPS*2008)
2007:
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Sufficient Conditions for Convergence of the Sum-Product AlgorithmIEEE Transactions on Information Theory 53(12):4422-4437, Dec. 2007
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Truncating the Loop Series Expansion for Belief PropagationJournal of Machine Learning Research 8(Sep):1987-2016, 2007
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Loop Corrections for Approximate Inference on Factor GraphsJournal of Machine Learning Research 8(May):1113-1143, 2007
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Inference in the Promedas medical expert systemProceedings of the 11th Conference on Artificial Intelligence in Medicine (AIME 07)
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Loop Corrected Belief PropagationProceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07)
2005:
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Sufficient conditions for convergence of Loopy Belief PropagationProceedings of the 21th Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)
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On the properties of the Bethe approximation and Loopy Belief Propagation on binary networks
2004:
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Validity Estimates for Loopy Belief Propagation on Binary Real-world NetworksAdvances in Neural Information Processing Systems 17 (NIPS*2004)
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Quantitative Imaging through a Spectrograph. 1. Principles and TheoryApplied Optics Volume 43, Issue 30, pp. 5669-5681 (October 2004)
Books
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Understanding and Improving Belief PropagationPhD thesis May 7, 2008
Technical reports
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Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
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Novel Bounds on Marginal ProbabilitiesarXiv:0801.3797v1, submitted to JMLR
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Loop corrections for approximate inferencearXiv:cs/0612030v1, technical report
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Spin-glass phase transitions on real-world graphs
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