Large-Scale Differentiable Causal Discovery of Factor Graphs. (arXiv:2206.07824v1 [stat.ML])

Web: http://arxiv.org/abs/2206.07824

A common theme in causal inference is learning causal relationships between
observed variables, also known as causal discovery. This is usually a daunting
task, given the large number of candidate causal graphs and the combinatorial
nature of the search space. Perhaps for this reason, most research has so far
focused on relatively small causal graphs, with up to hundreds of nodes.
However, recent advances in fields like biology enable generating experimental
data sets with thousands of interventions followed by rich …

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