55 citations · 171 across the 20 of their papers we have counts for
7 papers · 1 filter
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…
Bootstrap aggregation and confidence measures to improve time series causal discovery
Kevin Debeire, Jakob Runge, Andreas Gerhardus +1
Learning causal graphs from multivariate time series is a ubiquitous challenge in all application domains dealing with time-dependent systems, such as in Earth sciences, biology, o…
Causal inference for temporal patterns
Nicolas-Domenic Reiter, Andreas Gerhardus, Jakob Runge
Complex dynamical systems are prevalent in many scientific disciplines. In the analysis of such systems two aspects are of particular interest: 1) the temporal patterns along which…
Characterization of causal ancestral graphs for time series with latent confounders
Andreas Gerhardus
In this paper, we introduce a novel class of graphical models for representing time lag specific causal relationships and independencies of multivariate time series with unobserved…
High-recall causal discovery for autocorrelated time series with latent confounders
Andreas Gerhardus, Jakob Runge
We present a new method for linear and nonlinear, lagged and contemporaneous constraint-based causal discovery from observational time series in the presence of latent confounders.…