Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
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Joris M. Mooij | Sara Magliacane | Thijs van Ommen | Tom Claassen | Stephan Bongers | Philip Versteeg | J. Mooij | Philip Versteeg | T. V. Ommen | T. Claassen | Sara Magliacane | S. Bongers | Joris M. Mooij | Tom Claassen | Philip Versteeg | Sara Magliacane
[1] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[2] Bernhard Schölkopf,et al. Theoretical Aspects of Cyclic Structural Causal Models , 2016 .
[3] Judea Pearl,et al. Comment: Graphical Models, Causality and Intervention , 2016 .
[4] Elias Bareinboim,et al. Causal inference and the data-fusion problem , 2016, Proceedings of the National Academy of Sciences.
[5] T. Richardson. Markov Properties for Acyclic Directed Mixed Graphs , 2003 .
[6] Tom Burr,et al. Causation, Prediction, and Search , 2003, Technometrics.
[7] Neil D. Lawrence,et al. Dataset Shift in Machine Learning , 2009 .
[8] Joris M. Mooij,et al. Cyclic Causal Discovery from Continuous Equilibrium Data , 2013, UAI.
[9] J. Pearl. Causality: Models, Reasoning and Inference , 2000 .
[10] Bernhard Schölkopf,et al. Multi-Source Domain Adaptation: A Causal View , 2015, AAAI.
[11] Jiji Zhang,et al. On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias , 2008, Artif. Intell..
[12] Matti Järvisalo,et al. Learning Optimal Causal Graphs with Exact Search , 2018, PGM.
[13] Joris M. Mooij,et al. Joint Causal Inference from Multiple Contexts , 2016, J. Mach. Learn. Res..
[14] Elias Bareinboim,et al. A General Algorithm for Deciding Transportability of Experimental Results , 2013, ArXiv.
[15] Joris M. Mooij,et al. Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders , 2018, UAI.
[16] Frederick Eberhardt,et al. Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming , 2014, UAI.
[17] Rainer Spang,et al. Probabilistic Soft Interventions in Conditional Gaussian Networks , 2005, AISTATS.
[18] Bernhard Schölkopf,et al. Invariant Models for Causal Transfer Learning , 2015, J. Mach. Learn. Res..
[19] Kevin P. Murphy,et al. Exact Bayesian structure learning from uncertain interventions , 2007, AISTATS.
[20] Frederick Eberhardt,et al. Do-calculus when the True Graph Is Unknown , 2015, UAI.
[21] Ioannis Tsamardinos,et al. Constraint-based causal discovery from multiple interventions over overlapping variable sets , 2014, J. Mach. Learn. Res..
[22] Amos Storkey,et al. When Training and Test Sets are Different: Characterising Learning Transfer , 2013 .
[23] Elias Bareinboim,et al. Transportability of Causal and Statistical Relations: A Formal Approach , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.
[24] Gregory F. Cooper,et al. A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships , 1997, Data Mining and Knowledge Discovery.
[25] Motoaki Kawanabe,et al. Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation , 2007, NIPS.
[26] Neil D. Lawrence,et al. When Training and Test Sets Are Different: Characterizing Learning Transfer , 2009 .
[27] Jin Tian,et al. Causal Discovery from Changes , 2001, UAI.
[28] Bernhard Schölkopf,et al. Domain Adaptation with Conditional Transferable Components , 2016, ICML.
[29] Bernhard Schölkopf,et al. Domain Adaptation under Target and Conditional Shift , 2013, ICML.
[30] Yishay Mansour,et al. Domain Adaptation: Learning Bounds and Algorithms , 2009, COLT.
[31] Joris M. Mooij,et al. Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions , 2016, ArXiv.
[32] Joris M. Mooij,et al. Ancestral Causal Inference , 2016, NIPS.
[33] Bernhard Schölkopf,et al. On causal and anticausal learning , 2012, ICML.
[34] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.