  Reconstructing the causal relationships behind the phenomena we observe is a
fundamental challenge in all areas of science. Discovering causal relationships
through experiments is often infeasible, unethical, or expensive in complex
systems. However, increases in computational power allow us to process the
ever-growing amount of data that modern science generates, leading to an
emerging interest in the causal discovery problem from observational data. This
work evaluates the LPCMCI algorithm, which aims to find generators compatible
with a multi-dimensional, highly autocorrelated time series while some
variables are unobserved. We find that LPCMCI performs much better than a
random algorithm mimicking not knowing anything but is still far from optimal
detection. Furthermore, LPCMCI performs best on auto-dependencies, then
contemporaneous dependencies, and struggles most with lagged dependencies. The
source code of this project is available online.
