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Theory and Methods

Invariant Causal Prediction for Sequential Data

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Pages 1264-1276 | Received 01 Jun 2017, Published online: 13 Dec 2018

References

  • Bollen, K. A. (1989), Structural Equations with Latent Variables, New York: Wiley.
  • Bühlmann, P., Peters, J., and Ernest, J. (2014), “CAM: Causal Additive Models, High-dimensional Order Search and Penalized Regression,” Annals of Statistics, 42, 2526–2556.
  • Chickering, D. M. (2002), “Optimal Structure Identification with Greedy Search,” Journal of Machine Learning Research, 3, 507–554.
  • Chow, G. C. (1960), “Tests of Equality Between Sets of Coefficients in Two Linear Regressions,” Econometrica, 28, 591–605.
  • Chu, T., and Glymour, C. (2008), “Search for Additive Nonlinear Time Series Causal Models,” Journal of Machine Learning Research, 9, 967–991.
  • Eaton, D., and Murphy, K. P. (2007), “Exact Bayesian Structure Learning from Uncertain Interventions,” in Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 107–114.
  • Gong, M., Zhang, K., Schölkopf, B., Tao, D., and Geiger, P. (2015), “Discovering Temporal Causal Relations from Subsampled Data,” in Proceedings of the 32nd International Conference on Machine Learning (ICML) (Vol. 37), pp. 1898–1906.
  • Granger, C. W. J. (1969), “Investigating Causal Relations by Econometric Models and Cross-spectral Methods,” Econometrica, 37, 424–438.
  • Gretton, A., Fukumizu, K., Teo, C. H., Song, L., Schölkopf, B., and Smola, A. J. (2007), “A Kernel Statistical Test of Independence,” in Advances in Neural Information Processing Systems (NIPS) (Vol. 20), pp. 585–592.
  • Hauser, A., and Bühlmann, P. (2012), “Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs,” Journal of Machine Learning Research, 13, 2409–2464.
  • Hyvärinen, A., Shimizu, S., and Hoyer, P. (2008), “Causal Modelling Combining Instantaneous and Lagged Effects: An Identifiable Model Based on Non-Gaussianity,” in Proceedings of the 25th International Conference on Machine Learning (ICML), pp. 424–431.
  • Imbens, G. W., and Rubin, D. B. (2015), Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, New York: Cambridge University Press.
  • Janzing, D., Mooij, J. M., Zhang, K., Lemeire, J., Zscheischler, J., Daniusis, P., Steudel, B., and Schölkopf, B. (2012), “Information-Geometric Approach to Inferring causal Directions,” Artificial Intelligence, 182–183, 1–31.
  • Lütkepohl, H. (2005), New Introduction to Multiple Time Series Analysis, New York: Springer-Verlag.
  • Mooij, J. M., Janzing, D., Peters, J., and Schölkopf, B. (2009), “Regression by Dependence Minimization and its Application to Causal Inference,” in Proceedings of the 26th International Conference on Machine Learning (ICML), pp. 745–752.
  • Pearl, J. (2009), Causality: Models, Reasoning, and Inference ( 2nd ed.), New York: Cambridge University Press.
  • Peirce, C. S. (1883), “A Theory of Probable Inference,” in Studies in Logic by Members of the Johns Hopkins University, ed. C. S. Peirce, Boston: Little, Brown, and Company, pp. 126–181.
  • Peters, J., Bühlmann, P., and Meinshausen, N. (2016), “Causal Inference Using Invariant Prediction: Identification and Confidence Intervals” ( with discussion), Journal of the Royal Statistical Society, Series B, 78, 947–1012.
  • Peters, J., Janzing, D., and Schölkopf, B. (2013), “Causal Inference on Time Series Using Structural Equation Models,” in Advances in Neural Information Processing Systems (NIPS) (Vol. 26), Curran Associates, Inc., pp. 585–592.
  • ——— (2017), Elements of Causal Inference: Foundations and Learning Algorithms, Cambridge, MA: MIT Press.
  • Peters, J., Mooij, J. M., Janzing, D., and Schölkopf, B. (2014), “Causal Discovery with Continuous Additive Noise Models,” Journal of Machine Learning Research, 15, 2009–2053.
  • Pfister, N., Bühlmann, P., Schölkopf, B., and Peters, J. (2018), “Kernel-Based Tests for Joint Independence,” Journal of the Royal Statistical Society, Series B, 80, 5–31.
  • Shah, R., and Bühlmann, P. (2018), “Goodness-of-Fit Tests for High Dimensional Linear Models,” Journal of the Royal Statistical Society, Series B, 80, 113–135.
  • Shimizu, S., Hoyer, P., Hyvärinen, A., and Kerminen, A. J. (2006), “A Linear Non-Gaussian Acyclic Model for Causal Discovery,” Journal of Machine Learning Research, 7, 2003–2030.
  • Siracusa, M., and Fisher III, J. (2009), “Tractable Bayesian Inference of Time-Series Dependence Structure,” in Artificial Intelligence and Statistics, pp. 528–535.
  • Spirtes, P., Glymour, C., and Scheines, R. (2000), Causation, Prediction, and Search ( 2nd ed.), Cambridge, MA: MIT Press.
  • Talih, M., and Hengartner, N. (2005), “Structural Learning with Time-Varying Components: Tracking the Cross-Section of Financial Time Series,” Journal of the Royal Statistical Society, Series B, 67, 321–341.
  • Tank, A., Fox, E., and Shojaie, A. (2017), “Identifiability and Estimation of Structural Vector Autoregressive Models for Subsampled and Mixed Frequency Time Series,” ArXiv e-prints (1704.02519v1).
  • van de Geer, S., and Bühlmann, P. (2013), “ℓ0-Penalized Maximum Likelihood for Sparse Directed Acyclic Graphs,” Annals of Statistics, 41, 536–567.
  • Wood, S. N., and Augustin, N. H. (2002), “GAMs with Integrated Model Selection Using Penalized Regression Splines and Applications to Environmental Modelling,” Ecological Modelling, 157, 157–177.

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