Constraint-Based Causal Structure Learning from Undersampled Graphs. (arXiv:2205.09235v3 [stat.ML] UPDATED)

Graphical structures estimated by causal learning algorithms from time series
data can provide highly misleading causal information if the causal timescale
of the generating process fails to match the measurement timescale of the data.
Although this problem has been recently recognized, practitioners have limited
resources to respond to it, and so must continue using models that they know
are likely misleading. Existing methods either (a) require that the difference
between causal and measurement timescales is known; or (b) can handle …

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