Jakob Runge
19 papers · 2018–2025 · 8 conferences · across top CS/AI conferences
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CLEAR (4)
UAI (4)
AISTATS (2)
AAAI (1)
ECCV (1)
ICML (1)
JMLR (1)
Top co-authors
Keywords
causal discovery
(9)
time series
(4)
conditional independence
(4)
structural causal model
(3)
causal graph
(3)
graphical model
(3)
causal inference
(2)
conditional independence test
(2)
non-parametric method
(2)
nearest neighbor
(1)
partial correlation
(1)
benchmark evaluation
(1)
machine learning
(1)
latent variable
(1)
graph embedding
(1)
causal representation learning
(1)
mutual information
(1)
causal effect
(1)
statistical testing
(1)
causal effect estimation
(1)
Papers
Sanity Checking Causal Representation Learning on a Simple Real-World System
ICML 2025
Separation-Based Distance Measures for Causal Graphs
AISTATS 2025
Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery
CLEAR 2025
Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation
CLEAR 2025
A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs
UAI 2024
Causal discovery with endogenous context variables
NIPS 2024
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
CLEAR 2024
Bootstrap aggregation and confidence measures to improve time series causal discovery
CLEAR 2024
Increasing effect sizes of pairwise conditional independence tests between random vectors
UAI 2023
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
NIPS 2023
Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation
JMLR 2023
Causal Discovery for time series from multiple datasets with latent contexts
UAI 2023
Vector Causal Inference between Two Groups of Variables
AAAI 2023
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
NIPS 2022
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
NIPS 2021
High-recall causal discovery for autocorrelated time series with latent confounders
NIPS 2020
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
UAI 2020
Determining the Relevance of Features for Deep Neural Networks
ECCV 2020
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
AISTATS 2018