55 citations · 171 across the 20 of their papers we have counts for
20 papers
Identifying Direct Causal Effects in Latent Factor Models by Accounting for Unidentified Parents
Tom Hochsprung, Nils Sturma, Jakob Runge +2
We consider linear structural equation models with explicitly modelled latent variables. In such models, observed and latent variables solve linear equations including stochastic n…
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…
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…
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…