Causal discovery algorithms infer causal relations from data based on several
assumptions, including notably the absence of measurement error. However, this
assumption is most likely violated in practical applications, which may result
in erroneous, irreproducible results. In this work we show how to obtain an
upper bound for the variance of random measurement error from the covariance
matrix of measured variables and how to use this upper bound as a correction
for constraint-based causal discovery. We demonstrate a practical application
of our approach on both simulated data and real-world protein signaling data.Comment: Published in Proceedings of the 34th Annual Conference on Uncertainty
in Artificial Intelligence (UAI-18