128,612 research outputs found
Supervised estimation of Granger-based causality between time series
Brain effective connectivity aims to detect causal interactions between distinct brain units and it is typically studied through the analysis of direct measurements of the neural activity, e.g., magneto/electroencephalography (M/EEG) signals. The literature on methods for causal inference is vast. It includes model-based methods in which a generative model of the data is assumed and model-free methods that directly infer causality from the probability distribution of the underlying stochastic process. Here, we firstly focus on the model-based methods developed from the Granger criterion of causality, which assumes the autoregressive model of the data. Secondly, we introduce a new perspective, that looks at the problem in a way that is typical of the machine learning literature. Then, we formulate the problem of causality detection as a supervised learning task, by proposing a classification-based approach. A classifier is trained to identify causal interactions between time series for the chosen model and by means of a proposed feature space. In this paper, we are interested in comparing this classification-based approach with the standard Geweke measure of causality in the time domain, through simulation study. Thus, we customized our approach to the case of a MAR model and designed a feature space which contains causality measures based on the idea of precedence and predictability in time. Two variations of the supervised method are proposed and compared to a standard Granger causal analysis method. The results of the simulations show that the supervised method outperforms the standard approach, in particular it is more robust to noise. As evidence of the efficacy of the proposed method, we report the details of our submission to the causality detection competition of Biomag2014, where the proposed method reached the 2nd place. Moreover, as empirical application, we applied the supervised approach on a dataset of neural recordings of rats obtaining an important reduction in the false positive rate
Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data
The regression discontinuity (RD) design is a quasi-experimental design that
estimates the causal effects of a treatment by exploiting naturally occurring
treatment rules. It can be applied in any context where a particular treatment
or intervention is administered according to a pre-specified rule linked to a
continuous variable. Such thresholds are common in primary care drug
prescription where the RD design can be used to estimate the causal effect of
medication in the general population. Such results can then be contrasted to
those obtained from randomised controlled trials (RCTs) and inform prescription
policy and guidelines based on a more realistic and less expensive context. In
this paper we focus on statins, a class of cholesterol-lowering drugs, however,
the methodology can be applied to many other drugs provided these are
prescribed in accordance to pre-determined guidelines. NHS guidelines state
that statins should be prescribed to patients with 10 year cardiovascular
disease risk scores in excess of 20%. If we consider patients whose scores are
close to this threshold we find that there is an element of random variation in
both the risk score itself and its measurement. We can thus consider the
threshold a randomising device assigning the prescription to units just above
the threshold and withholds it from those just below. Thus we are effectively
replicating the conditions of an RCT in the area around the threshold, removing
or at least mitigating confounding. We frame the RD design in the language of
conditional independence which clarifies the assumptions necessary to apply it
to data, and which makes the links with instrumental variables clear. We also
have context specific knowledge about the expected sizes of the effects of
statin prescription and are thus able to incorporate this into Bayesian models
by formulating informative priors on our causal parameters.Comment: 21 pages, 5 figures, 2 table
Flux Analysis in Process Models via Causality
We present an approach for flux analysis in process algebra models of
biological systems. We perceive flux as the flow of resources in stochastic
simulations. We resort to an established correspondence between event
structures, a broadly recognised model of concurrency, and state transitions of
process models, seen as Petri nets. We show that we can this way extract the
causal resource dependencies in simulations between individual state
transitions as partial orders of events. We propose transformations on the
partial orders that provide means for further analysis, and introduce a
software tool, which implements these ideas. By means of an example of a
published model of the Rho GTP-binding proteins, we argue that this approach
can provide the substitute for flux analysis techniques on ordinary
differential equation models within the stochastic setting of process algebras
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