15 citations · 48 across the 16 of their papers we have counts for
16 papers
Learning Causal Structure of Time Series using Best Order Score Search
Irene Gema Castillo Mansilla, Urmi Ninad
Causal structure learning from observational data is central to many scientific and policy domains, but the time series setting common to many disciplines poses several challenges…
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…
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…
SpaceTime: Causal Discovery from Non-Stationary Time Series
Sarah Mameche, Lénaïg Cornanguer, Urmi Ninad +1
Understanding causality is challenging and often complicated by changing causal relationships over time and across environments. Climate patterns, for example, shift over time with…
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…
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…
Causal discovery for time series from multiple datasets with latent contexts
Wiebke Günther, Urmi Ninad, Jakob Runge
Causal discovery from time series data is a typical problem setting across the sciences. Often, multiple datasets of the same system variables are available, for instance, time ser…
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
Wiebke Günther, Urmi Ninad, jonas Wahl +1
Conditional independence (CI) testing is frequently used in data analysis and machine learning for various scientific fields and it forms the basis of constraint-based causal disco…
Foundations of Causal Discovery on Groups of Variables
Jonas Wahl, Urmi Ninad, Jakob Runge
Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models.…
Discovering Causal Relations and Equations from Data
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad +7
Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the p…
Vector causal inference between two groups of variables
Jonas Wahl, Urmi Ninad, Jakob Runge
Methods to identify cause-effect relationships currently mostly assume the variables to be scalar random variables. However, in many fields the objects of interest are vectors or g…
BPS Indices, Modularity and Perturbations in Quantum K-theory
Hans Jockers, Peter Mayr, Urmi Ninad +1
We study a perturbation family of N=2 3d gauge theories and its relation to quantum K-theory. A 3d version of the Intriligator-Vafa formula is given for the quantum K-theory ring o…
Wilson loop algebras and quantum K-theory for Grassmannians
Hans Jockers, Peter Mayr, Urmi Ninad +1
We study the algebra of Wilson line operators in three-dimensional N=2 supersymmetric U(M) gauge theories with a Higgs phase related to a complex Grassmannian Gr(M,N), and its conn…
The Geometry of Gauged Linear Sigma Model Correlation Functions
Andreas Gerhardus, Hans Jockers, Urmi Ninad
Applying advances in exact computations of supersymmetric gauge theories, we study the structure of correlation functions in two-dimensional N=(2,2) Abelian and non-Abelian gauge t…