Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding


Daniel Malinsky, Peter Spirtes ;
Proceedings of 2018 ACM SIGKDD Workshop on Causal Disocvery, PMLR 92:23-47, 2018.


We present constraint-based and (hybrid) score-based algorithms for causal structure learning that estimate dynamic graphical models from multivariate time series data. In contrast to previous work, our methods allow for both “contemporaneous” causal relations and arbitrary unmeasured (“latent”) processes influencing observed variables. The performance of our algorithms is investigated with simulation experiments and we briefly illustrate the proposed approach on some real data from international political economy.

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