Abstract: Causal inference with spatial environmental data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and non-local ...
Scientific modeling faces a tradeoff between the interpretability of mechanistic theory and the predictive power of machine learning. While existing hybrid approaches have made progress by ...
How does one model a simple cell-signaling pathway? Consider a simple example consisting of a stimulant, an extracellular signal, an inhibitor of the signal, a G protein–coupled receptor, a G protein ...
The total amount of money invested in the project. The total cost includes EU contribution as well as other project costs not covered by EU funding. It is expressed in euros. Projects in the first ...
Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...
The Frontier of Avian Observation Data and Probabilistic Machine Learning—From Hierarchical Bayes to Causal, Geometric, ...
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