1 citations · 2 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errors
Sofia Faltenbacher, Jonas Wahl, Rebecca Herman +1
Causal discovery methods based on the PC algorithm are proven to be sound if all structural assumptions are fulfilled and all conditional independence tests are correct. This ideal…