Causality in Time Series - ClopiNet

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Moneta Chlass Entner HoyerA. Yatchew. Nonparametric regression techniques in economics. Journal of EconomicLiterature, 36(2):669–721, 1998.130
JMLR: Workshop and Conference Proceedings 12:115–139, 2011Causality in Time SeriesTime Series Analysis with the Causality WorkbenchIsabelle Guyonisabelle@clopinet.comClopiNet, Berkeley, CaliforniaAlexander StatnikovAlexander.Statnikov@nyumc.orgNYU Langone Medical Center, New York cityConstantin AliferisConstantin.Aliferis@nyumc.orgNYU Center for Health Informatics and Bioinformatics, New York cityEditors: Florin Popescu and Isabelle GuyonAbstractThe Causality Workbench project is an environment to test causal discovery algorithms.Via a web portal (http://clopinet.com/causality), it provides anumber of resources, including a repository of datasets, models, and software packages,and a virtual laboratory allowing users to benchmark causal discovery algorithmsby performing virtual experiments to study artificial causal systems. We regularlyorganize competitions. In this paper, we describe what the platform offers forthe analysis of causality in time series analysis.Keywords: Causality, Benchmark, Challenge, Competition, Time Series Prediction.1. IntroductionUncovering cause-effect relationships is central in many aspects of everyday life in bothhighly industrialized and developing countries: what affects our health, the economy,climate changes, world conflicts, and which actions have beneficial effects? Establishingcausality is critical to guiding policy decisions in areas including medicine andpharmacology, epidemiology, climatology, agriculture, economy, sociology, law enforcement,and manufacturing.One important goal of causal modeling is to predict the consequences of given actions,also called interventions, manipulations or experiments. This is fundamentallydifferent from the classical machine learning, statistics, or data mining setting, whichfocuses on making predictions from observations. Observations imply no manipulationon the system under study whereas actions introduce a disruption in the naturalfunctioning of the system. In the medical domain, this is the distinction made between“diagnosis” and “prognosis” (prediction from observations of diseases or disease evolution)and “treatment” (intervention). For instance, smoking and coughing might be bothpredictive of respiratory disease and helpful for diagnosis purposes. However, if smokingis a cause and coughing a consequence, acting on the cause (smoking) can changeyour health status, but not acting on the symptom or consequence (coughing). Thus itc○ 2011 I. Guyon, A. Statnikov & C. Aliferis.
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Causality in Time SeriesVolume 5:Ca
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PrefaceThe following is a print ver
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JMLR: Workshop and Conference Proce
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Linking Granger Causality and the P
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Popescu1. IntroductionCausality is
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Popescugeneral terms, if we are pre
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PopescuType III error prob. γ = P
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Popescuwhich would correspond to a
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Popescuvalued vector. A Data Genera
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Popescuy i =K∑︁A k y i−k + Bu
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Popescuy i =K∑︁A k y i−k + Bu
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PopescuFigure 1: SVAR causality and
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Popescu6. Spectral methods and phas
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PopescuH GCj→i|u = logD i − log
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Popescu8.1. The cardinal transform
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Popescuy N,i = Bx N,i (37)y = (1
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Popescu(a) Unmixed colored noise(b)
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PopescuAs we can see in both Figure
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Popescuβ > 0 y C,i =x N,i =⎡K∑
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Popescupretation of information flo
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Popescuit is suggested that a princ
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PopescuA. N. Kolmogorov and A. N. S
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PopescuH. White and X. Lu. Granger
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PopescuTable 6: α vs. ΨN * → 50
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Roebroeck Seth Valdes-Sosaincreasin
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Roebroeck Seth Valdes-SosaFigure 1:
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