A constraint optimization approach to causal discovery from subsampled time series data

We consider causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system's causal structure if not properly taken into account. In this paper, we first consider the search for system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. We then apply the method to real-world data, investigate the robustness and scalability of our method, consider further approaches to reduce underdetermination in the output, and perform an extensive comparison between different solvers on this inference problem. Overall, these advances build towards a full understanding of non-parametric estimation of system timescale causal structures from subsampled time series data. •We present findings towards a full understanding of non-parametric estimation of causal structures from subsampled time series data.•We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches.•We propose the first constraint optimization approach for recovering system timescale causal structure.•We apply the approach to real-world data.•We show the robustness and scalability of our method with extensive simulations.