Selecting causal brain features with a single conditional independence test per feature

Atalanti A Mastakouri, Bernhard Schölkopf, Dominik Janzing

Abstract: We propose a constraint-based causal feature selection method for identifying causes of a given target variable, selecting from a set of candidate variables, while there can also be hidden variables acting as common causes with the target. We prove that if we observe a cause for each candidate cause, then a single conditional independence test with one conditioning variable is sufficient to decide whether a candidate associated with the target is indeed causing it. We thus improve upon existing methods by significantly simplifying statistical testing and requiring a weaker version of causal faithfulness. Our main assumption is inspired by neuroscience paradigms where the activity of a single neuron is considered to be also caused by its own previous state. We demonstrate successful application of our method to simulated as well as encephalographic data that we recorded from twenty-one participants. The detected causes of motor performance are in accordance with the latest consensus about the neurophysiological pathways, and can provide new insights into personalised brain stimulation.

Code Link: https://gitlab.tuebingen.mpg.de/amastakouri/singleCICausalFeatureSelection

CMT Num: 6822