Self-Compatibility: Evaluating Causal Discovery without Ground Truth

1 code implementation • 18 Jul 2023 • Philipp M. Faller, Leena Chennuru Vankadara, Atalanti A. Mastakouri, Francesco Locatello, Dominik Janzing

In this work, we propose a novel method for falsifying the output of a causal discovery algorithm in the absence of ground truth.

Causal Discovery Model Selection

Reinterpreting causal discovery as the task of predicting unobserved joint statistics

no code implementations • 11 May 2023 • Dominik Janzing, Philipp M. Faller, Leena Chennuru Vankadara

Here, causal discovery becomes more modest and better accessible to empirical tests than usual: rather than trying to find a causal hypothesis that is `true' a causal hypothesis is {\it useful} whenever it correctly predicts statistical properties of unobserved joint distributions.

Causal Discovery Causal Inference +1

Quantifying intrinsic causal contributions via structure preserving interventions

no code implementations • 1 Jul 2020 • Dominik Janzing, Patrick Blöbaum, Atalanti A. Mastakouri, Philipp M. Faller, Lenon Minorics, Kailash Budhathoki

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.