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Philipp M. Faller

2 accepted papers

2024

Quantifying intrinsic causal contributions via structure preserving interventions

AISTATS 2024poster

We propose a notion of causal influence that describes the ‘intrinsic’ part of the contribution of a node on a target node in a DAG. By recursively writing each node as a function of the upstream noise terms, we separate the intrinsic information added by each node from the one obtained from its anc…

Cited by 11SourcePDFScholar
2024

Self-Compatibility: Evaluating Causal Discovery without Ground Truth

AISTATS 2024poster

As causal ground truth is incredibly rare, causal discovery algorithms are commonly only evaluated on simulated data. This is concerning, given that simulations reflect preconceptions about generating processes regarding noise distributions, model classes, and more. In this work, we propose a novel…