25 citations · 27 across the 13 of their papers we have counts for
13 papers
Structural Causal Bottleneck Models
Simon Bing, Jonas Wahl, Jakob Runge
We introduce structural causal bottleneck models (SCBMs), a novel class of structural causal models in which causal effects between high-dimensional variables are mediated by low-d…
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…
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity
Yahya Aalaila, Gerrit Großmann, Sumantrak Mukherjee +2
Counterfactual reasoning, a cornerstone of human cognition and decision-making, is often seen as the 'holy grail' of causal learning, with applications ranging from interpreting ma…
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…
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard, Chandler Squires, Jonas Wahl +4
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world a…
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…
Invariance & Causal Representation Learning: Prospects and Limitations
Simon Bing, Jonas Wahl, Urmi Ninad +1
In causal models, a given mechanism is assumed to be invariant to changes of other mechanisms. While this principle has been utilized for inference in settings where the causal var…
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
Simon Bing, Urmi Ninad, Jonas Wahl +1
The task of inferring high-level causal variables from low-level observations, commonly referred to as causal representation learning, is fundamentally underconstrained. As such, r…
Projecting infinite time series graphs to finite marginal graphs using number theory
Andreas Gerhardus, Jonas Wahl, Sofia Faltenbacher +2
In recent years, a growing number of method and application works have adapted and applied the causal-graphical-model framework to time series data. Many of these works employ time…
Explainability Auditing for Intelligent Systems: A Rationale for Multi-Disciplinary Perspectives
Markus Langer, Kevin Baum, Kathrin Hartmann +3
National and international guidelines for trustworthy artificial intelligence (AI) consider explainability to be a central facet of trustworthy systems. This paper outlines a multi…