An Upper Bound for Random Measurement Error in Causal Discovery

TL;DR

This paper proposes a method to estimate an upper bound on measurement error variance from data covariance, improving causal discovery accuracy when measurement error is present.

Contribution

It introduces a novel approach to bound measurement error variance and applies it to enhance causal discovery in practical datasets.

Findings

Upper bound on measurement error variance can be derived from covariance matrices.

Applying the bound improves causal discovery accuracy.

Method validated on simulated and real-world data.

Abstract

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.