Investigating the effect of binning on causal discovery

TL;DR

This study investigates how binning continuous data affects the performance of the GES causal discovery algorithm, revealing that unbinned data generally perform better but are more sensitive to sample size and parameters.

Contribution

It provides the first systematic analysis of binning effects on causal discovery algorithms, highlighting the conditions under which binning impacts performance.

Findings

Unbinned data often yield higher search performance.

Binned data are more sensitive to sample size and tuning parameters.

Interactive effects exist between sample size, binning, and tuning parameters.

Abstract

Binning (a.k.a. discretization) of numerically continuous measurements is a wide-spread but controversial practice in data collection, analysis, and presentation. The consequences of binning have been evaluated for many different kinds of data analysis methods, however so far the effect of binning on causal discovery algorithms has not been directly investigated. This paper reports the results of a simulation study that examined the effect of binning on the Greedy Equivalence Search (GES) causal discovery algorithm. Our findings suggest that unbinned continuous data often result in the highest search performance, but some exceptions are identified. We also found that binned data are more sensitive to changes in sample size and tuning parameters, and identified some interactive effects between sample size, binning, and tuning parameter on performance.