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Technol."],"published-print":{"date-parts":[[2016,1,22]]},"abstract":"<jats:p>In applied fields, practitioners hoping to apply causal structure learning or causal orientation algorithms face an important question: which independence test is appropriate for my data? In the case of real-valued iid data, linear dependencies, and Gaussian error terms, partial correlation is sufficient. But once any of these assumptions is modified, the situation becomes more complex. Kernel-based tests of independence have gained popularity to deal with nonlinear dependencies in recent years, but testing for conditional independence remains a challenging problem. We highlight the important issue of non-iid observations: when data are observed in space, time, or on a network, \u201cnearby\u201d observations are likely to be similar. This fact biases estimates of dependence between variables. Inspired by the success of Gaussian process regression for handling non-iid observations in a wide variety of areas and by the usefulness of the Hilbert-Schmidt Independence Criterion (HSIC), a kernel-based independence test, we propose a simple framework to address all of these issues: first, use Gaussian process regression to control for certain variables and to obtain residuals. Second, use HSIC to test for independence. We illustrate this on two classic datasets, one spatial, the other temporal, that are usually treated as iid. We show how properly accounting for spatial and temporal variation can lead to more reasonable causal graphs. We also show how highly structured data, like images and text, can be used in a causal inference framework using a novel structured input\/output Gaussian process formulation. We demonstrate this idea on a dataset of translated sentences, trying to predict the source language.<\/jats:p>","DOI":"10.1145\/2806892","type":"journal-article","created":{"date-parts":[[2015,11,30]],"date-time":"2015-11-30T19:03:44Z","timestamp":1448910224000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference"],"prefix":"10.1145","volume":"7","author":[{"given":"Seth R.","family":"Flaxman","sequence":"first","affiliation":[{"name":"Machine Learning Department and Event and Pattern Detection Laboratory, H. J. Heinz III College, Carnegie Mellon University, Pittsburgh, PA"}]},{"given":"Daniel B.","family":"Neill","sequence":"additional","affiliation":[{"name":"Event and Pattern Detection Laboratory, H. J. Heinz III College, Carnegie Mellon University, Pittsburgh, PA"}]},{"given":"Alexander J.","family":"Smola","sequence":"additional","affiliation":[{"name":"Machine Learning Department, Carnegie Mellon University, Marianas Labs, Pittsburgh, PA"}]}],"member":"320","published-online":{"date-parts":[[2015,11,26]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Advances in Neural Information Processing Systems 26","author":"Besserve Michel","unstructured":"Michel Besserve , Nikos K. Logothetis , and Bernhard Sch\u00f6lkopf . 2013. Statistical analysis of coupled time series with kernel cross-spectral density operators . In Advances in Neural Information Processing Systems 26 , C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger (Eds.). Curran Associates, Inc. , 2535--2543. Michel Besserve, Nikos K. Logothetis, and Bernhard Sch\u00f6lkopf. 2013. Statistical analysis of coupled time series with kernel cross-spectral density operators. 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Salkauskas. 1982. Some relationships between surface splines and kriging. In Multivariate Approximation Theory II, W. Schempp and K. Zeller (Eds.). Birkhauser, Basel, 313--325."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-75225-7_5"},{"key":"e_1_2_1_33_1","doi-asserted-by":"crossref","unstructured":"P. Spirtes C. Glymour and R. Scheines. 2001. Causation Prediction and Search. Vol. 81. MIT Press.  P. Spirtes C. Glymour and R. Scheines. 2001. Causation Prediction and Search. Vol. 81. MIT Press.","DOI":"10.7551\/mitpress\/1754.001.0001"},{"key":"e_1_2_1_34_1","first-page":"1517","article-title":"Hilbert space embeddings and metrics on probability measures","volume":"99","author":"Sriperumbudur Bharath K.","year":"2010","unstructured":"Bharath K. Sriperumbudur , Arthur Gretton , Kenji Fukumizu , Bernhard Sch\u00f6lkopf , and Gert R. G. Lanckriet . 2010 . Hilbert space embeddings and metrics on probability measures . 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J\u00f6rg Tiedemann. 2009. News from OPUS - A collection of multilingual parallel corpora with tools and interfaces. In Recent Advances in Natural Language Processing, N. Nicolov, K. Bontcheva, G. Angelova, and R. Mitkov (Eds.). Vol. V. John Benjamins, Amsterdam\/Philadelphia, Borovets, Bulgaria, 237--248."},{"key":"e_1_2_1_38_1","unstructured":"Robert E. Tillman Arthur Gretton and Peter Spirtes. 2009. Nonlinear directed acyclic structure learning with weakly additive noise models. In NIPS. 1847--1855.  Robert E. Tillman Arthur Gretton and Peter Spirtes. 2009. Nonlinear directed acyclic structure learning with weakly additive noise models. In NIPS. 1847--1855."},{"key":"e_1_2_1_39_1","first-page":"2095","article-title":"Information rates of nonparametric Gaussian process methods","volume":"12","author":"Der Vaart Aad Van","year":"2011","unstructured":"Aad Van Der Vaart and Harry Van Zanten . 2011 . Information rates of nonparametric Gaussian process methods . The Journal of Machine Learning Research 12 (2011), 2095 -- 2119 . Aad Van Der Vaart and Harry Van Zanten. 2011. Information rates of nonparametric Gaussian process methods. The Journal of Machine Learning Research 12 (2011), 2095--2119.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.5555\/2567709.2502617"},{"key":"e_1_2_1_41_1","volume-title":"Spline Models for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics","author":"Wahba G.","unstructured":"G. Wahba . 1990. Spline Models for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics , Vol. 59 . SIAM , Philadelphia . G. Wahba. 1990. Spline Models for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 59. SIAM, Philadelphia."},{"key":"e_1_2_1_42_1","volume-title":"Learning and Inference in Graphical Models","author":"Williams C. K. I.","unstructured":"C. K. I. 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