Causal Structure Learning from Time Series Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding

We present constraint-based and (hybrid) score-based algorithms for causal structure learning that estimate dynamic graphical models from multivariate time series data. In contrast to previous work, our methods allow for both “contemporaneous” causal relations and arbitrary unmeasured (“latent”) processes influencing observed variables. The performance of our algorithms is investigated with simulation experiments and we briefly illustrate the proposed approach on some real data from international political economy.

[1]  Pentti Saikkonen,et al.  Stability results for nonlinear vector autoregressions with an application to a nonlinear error correction model , 2001 .

[2]  David A. Bessler,et al.  Money and prices: U.S. Data 1869–1914 (A study with directed graphs) , 2002 .

[3]  Wei Gao,et al.  Latent ancestral graph of structure vector autoregressive models , 2010 .

[4]  Daniel Malinsky,et al.  Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD , 2016, 1607.08110.

[5]  Clark Glymour,et al.  Search for Additive Nonlinear Time Series Causal Models , 2008, J. Mach. Learn. Res..

[6]  P. Spirtes,et al.  MARKOV EQUIVALENCE FOR ANCESTRAL GRAPHS , 2009, 0908.3605.

[7]  Bernhard Schölkopf,et al.  Discovering Temporal Causal Relations from Subsampled Data , 2015, ICML.

[8]  Bernhard Schölkopf,et al.  Elements of Causal Inference: Foundations and Learning Algorithms , 2017 .

[9]  K. Hoover,et al.  Searching for the Causal Structure of a Vector Autoregression , 2003 .

[10]  M. Maathuis,et al.  Estimating high-dimensional intervention effects from observational data , 2008, 0810.4214.

[11]  Aapo Hyvärinen,et al.  Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity , 2010, J. Mach. Learn. Res..

[12]  Deborah Mitchell,et al.  Globalization, government spending and taxation in the OECD , 2001 .

[13]  Patrik O. Hoyer,et al.  Causal Search in Structural Vector Autoregressive Models , 2009, NIPS Mini-Symposium on Causality in Time Series.

[14]  P. Spirtes,et al.  Ancestral graph Markov models , 2002 .

[15]  Bernhard Schölkopf,et al.  Causal Discovery from Temporally Aggregated Time Series , 2017, UAI.

[16]  Kevin P. Murphy,et al.  Learning the Structure of Dynamic Probabilistic Networks , 1998, UAI.

[17]  Bernhard Schölkopf,et al.  Causal Inference on Time Series using Restricted Structural Equation Models , 2013, NIPS.

[18]  David Maxwell Chickering,et al.  Optimal Structure Identification With Greedy Search , 2002, J. Mach. Learn. Res..

[19]  Peter Spirtes,et al.  Directed Cyclic Graphical Representations of Feedback Models , 1995, UAI.

[20]  Helmut Ltkepohl,et al.  New Introduction to Multiple Time Series Analysis , 2007 .

[21]  Peter Spirtes,et al.  A Hybrid Causal Search Algorithm for Latent Variable Models , 2016, Probabilistic Graphical Models.

[22]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[23]  Vince D. Calhoun,et al.  Rate-Agnostic (Causal) Structure Learning , 2015, NIPS.

[24]  Norman R. Swanson,et al.  Impulse Response Functions Based on a Causal Approach to Residual Orthogonalization in Vector Autoregressions , 1997 .

[25]  P. Hoyer,et al.  On Causal Discovery from Time Series Data using FCI , 2010 .

[26]  C. W. J. Granger,et al.  Economic Processes Involving Feedback , 1963, Inf. Control..

[27]  Sergey M. Plis,et al.  A constraint optimization approach to causal discovery from subsampled time series data , 2017, Int. J. Approx. Reason..

[28]  M. Eichler.  Causal inference in time series analysis , 2012 .

[29]  F. Fisher.  A Correspondence Principle for Simultaneous Equation Models , 1970 .

[30]  Russell A. Poldrack,et al.  Six problems for causal inference from fMRI , 2010, NeuroImage.

[31]  Jonathan N. Katz,et al.  Modeling Dynamics in Time-SeriesCross-Section Political Economy Data , 2011 .

[32]  C. Granger.  Some recent development in a concept of causality , 1988 .

[33]  Jiji Zhang,et al.  Causal Reasoning with Ancestral Graphs , 2008, J. Mach. Learn. Res..

[34]  Thomas S. Richardson,et al.  Iterative Conditional Fitting for Gaussian Ancestral Graph Models , 2004, UAI.

[35]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[36]  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..