Year: 2019
Type: article
Abstract: We present a sample path dependent measure of causal influence between time series. The proposed causal measure is a random sequence, a realization of which enables identification of specific patterns that give rise to high levels of causal influence. We show that these patterns cannot be identified by existing measures such as directed information (DI). We demonstrate how sequential prediction theory may be leveraged to estimate the proposed causal measure and introduce a notion of regret for assessing the performance of such estimators. We prove a finite sample bound on this regret that is determined by the worst case regret of the sequential predictors used in the estimator. Justification for the proposed measure is provided through a series of examples, simulations, and application to stock market data. Within the context of estimating DI, we show that, because joint Markovicity of a pair of processes does not imply the marginal Markovicity of individual processes, commonly used plug-in estimators of DI will be biased for a large subset of jointly Markov processes. We introduce a notion of DI with “stale history”, which can be combined with a plug-in estimator to upper and lower bound the DI when marginal Markovicity does not hold.
Authors: Gabriel Schamberg, Todd P. Coleman
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
Awards: G1513559549, G1957798471, G3112872626, Behavioral, Physiological, and Quantitative Models of Pro-Social Behavior, G408915191, G5382857288, G5385673730, Emerging Frontiers of Science of Information, G7452299184, SCH: INT: Collaborative Research: Replicating Clinic Physical Therapy at Home: Touch, Depth, and Epidermal Electronics in an Interactive Avatar System, G8060789275, G8257659198
OpenAlex ID: W2897370233