Temporally Causal Discovery Tests for Discrete Time Series and Neural Spike Trains

A. Theocharous, G. G. Gregoriou, P. Sapountzis, I. Kontoyiannis

Publication date: 23 May 2023

Abstract: We consider the problem of detecting causal relationships between discrete time series, in the presence of potential confounders. A hypothesis test is introduced for identifying the temporally causal influence of

(x_{n})

on

(y_{n})

, causally conditioned on a possibly confounding third time series

(z_{n})

. Under natural Markovian modeling assumptions, it is shown that the null hypothesis, corresponding to the absence of temporally causal influence, is equivalent to the underlying `causal conditional directed information rate' being equal to zero. The plug-in estimator for this functional is identified with the log-likelihood ratio test statistic for the desired test. This statistic is shown that is asymptotically normal under the alternative hypothesis and asymptotically

c h i^{2}

distributed under the null, facilitating the computation of

p

-values when used on empirical data. The effectiveness of the resulting hypothesis test is illustrated on simulated data, validating the underlying theory. The test is also employed in the analysis of spike train data recorded from neurons in the V4 and FEF brain regions of behaving animals during a visual attention task. There, the test results are seen to identify interesting and biologically relevant information.

Has companion code repository: https://github.com/andreas947/github_temporal_causality

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