1 citations · 2 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support
Martin Rabel, Wiebke Günther, Jakob Runge +1
Causal structure learning with data from multiple contexts carries both opportunities and challenges. Opportunities arise from considering shared and context-specific causal graphs…
Asymptotic Uncertainty in the Estimation of Frequency Domain Causal Effects for Linear Processes
Nicolas-Domenic Reiter, Jonas Wahl, Gabriele C. Hegerl +1
Structural vector autoregressive (SVAR) processes are commonly used time series models to identify and quantify causal interactions between dynamically interacting processes from o…
Causal Inference on Process Graphs, Part II: Causal Structure and Effect Identification
Nicolas-Domenic Reiter, Jonas Wahl, Andreas Gerhardus +1
A structural vector autoregressive (SVAR) process is a linear causal model for variables that evolve over a discrete set of time points and between which there may be lagged and in…
Separation-based distance measures for causal graphs
Jonas Wahl, Jakob Runge
Assessing the accuracy of the output of causal discovery algorithms is crucial in developing and comparing novel methods. Common evaluation metrics such as the structural Hamming d…