Causal Discovery in the Presence of Measurement Error
Tineke Blom, Sara Magliacane, Anna Klimovskaia, Joris M. Mooij · Uncertainty in Artificial Intelligence · 2018
Causal discovery algorithms can infer causal relationships based on several assumptions, which include the absence of measurement error. However, this assumption is most likely violated in practical applications, resulting in erroneous, irreproducible results. In this work we show how an upper-bound for the variance of random measurement error can be obtained from the covariance matrix of measured variables. We demonstrate a practical application of our approach on real-world protein signaling data.