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Graphical structures estimated by causal learning algorithms from time seriesdata can provide highly misleading causal information if the causal timescaleof the generating process fails to match the measurement timescale of the data.Although this problem has been recently recognized, practitioners have limitedresources to respond to it, and so must continue using models that they knoware likely misleading. Existing methods either (a) require that the differencebetween causal and measurement timescales is known; or (b) can handle only verysmall number of random variables when the timescale difference is unknown; or(c) apply to only pairs of variables, though with fewer assumptions about priorknowledge; or (d) return impractically too many solutions. This paper addressesall four challenges. We combine constraint programming with both theoreticalinsights into the problem structure and prior information about admissiblecausal interactions. The resulting system provides a practical approach thatscales to significantly larger sets (>100) of random variables, does notrequire precise knowledge of the timescale difference, supports edgemisidentification and parametric connection strengths, and can provide theoptimum choice among many possible solutions. The cumulative impact of theseimprovements is gain of multiple orders of magnitude in speed andinformativeness.

Constraint-Based Causal Structure Learning from Undersampled Graphs