4 papers
Using Time Structure to Estimate Causal Effects
Tom Hochsprung, Jakob Runge, Andreas Gerhardus
There exist several approaches for estimating causal effects in time series when latent confounding is present. Many of these approaches rely on additional auxiliary observed varia…
Causal discovery on vector-valued variables and consistency-guided aggregation
Urmi Ninad, Jonas Wahl, Andreas Gerhardus +1
Causal discovery (CD) aims to discover the causal graph underlying the data generation mechanism of observed variables. In many real-world applications, the observed variables are…
Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery
Rebecca J. Herman, Jonas Wahl, Urmi Ninad +1
Causal discovery aims to extract qualitative causal knowledge in the form of causal graphs from data. Because causal ground truth is rarely known in the real world, simulated data…
Causal discovery with endogenous context variables
Wiebke Günther, Oana-Iuliana Popescu, Martin Rabel +3
Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or intern…