no code implementations • 7 Feb 2024 • jonas Wahl, Jakob Runge
Many state-of-the-art causal discovery methods aim to generate an output graph that encodes the graphical separation and connection statements of the causal graph that underlies the data-generating process.
no code implementations • 6 Dec 2023 • Simon Bing, jonas Wahl, Urmi Ninad, Jakob Runge
In causal models, a given mechanism is assumed to be invariant to changes of other mechanisms.
1 code implementation • 5 Nov 2023 • Simon Bing, Urmi Ninad, jonas Wahl, Jakob Runge
The task of inferring high-level causal variables from low-level observations, commonly referred to as causal representation learning, is fundamentally underconstrained.
no code implementations • 9 Oct 2023 • Andreas Gerhardus, jonas Wahl, Sofia Faltenbacher, Urmi Ninad, Jakob Runge
In this work, we develop a method for projecting infinite time series graphs with repetitive edges to marginal graphical models on a finite time window.
no code implementations • 20 Jun 2023 • Wiebke Günther, Urmi Ninad, jonas Wahl, Jakob Runge
We frame heteroskedasticity in a structural causal model framework and present an adaptation of the partial correlation CI test that works well in the presence of heteroskedastic noise, given that expert knowledge about the heteroskedastic relationships is available.