Additive noise models for photoacoustic spatial coherence theory

Development and validation of a short-lag spatial coherence theory for photoacoustic imaging

We previously derived a theoretical expression for spatial coherence in photoacoustic data and utilized it to study properties of the short-lag spatial coherence (SLSC) beamformer applied to photoacoustic imaging. In this paper, an updated version of our theoretical equation is evaluated to generate SLSC images of a point target, a 1.2 mm diameter target, and corresponding lateral profiles thro...

Causal discovery with continuous additive noise models

We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural equation model with an additive noise structure, the directed acyclic graph becomes identifiable fro...

Nonlinear causal discovery with additive noise models

The discovery of causal relationships between a set of observed variables is a fundamental problem in science. For continuous-valued data linear acyclic causal models with additive noise are often used because these models are well understood and there are well-known methods to fit them to data. In reality, of course, many causal relationships are more or less nonlinear, raising some doubts as ...

Identifying confounders using additive noise models

We propose a method for inferring the existence of a latent common cause (“confounder”) of two observed random variables. The method assumes that the two effects of the confounder are (possibly nonlinear) functions of the confounder plus independent, additive noise. We discuss under which conditions the model is identifiable (up to an arbitrary reparameterization of the confounder) from the joi...

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