29 citations · 43 across the 13 of their papers we have counts for
13 papers
Exact Graph Learning via Integer Programming
Lucas Kook, Søren Wengel Mogensen
Learning the dependence structure among variables in complex systems is a central problem across medical, natural, and social sciences. These structures can be naturally represente…
An Asymmetric Independence Model for Causal Discovery on Path Spaces
Georg Manten, Cecilia Casolo, Søren Wengel Mogensen +1
We develop the theory linking 'E-separation' in directed mixed graphs (DMGs) with conditional independence relations among coordinate processes in stochastic differential equations…
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Georg Manten, Cecilia Casolo, Emilio Ferrucci +3
Inferring the causal structure underlying stochastic dynamical systems from observational data holds great promise in domains ranging from science and health to finance. Such proce…
Causal discovery in a complex industrial system: A time series benchmark
Søren Wengel Mogensen, Karin Rathsman, Per Nilsson
Causal discovery outputs a causal structure, represented by a graph, from observed data. For time series data, there is a variety of methods, however, it is difficult to evaluate t…
Faithful graphical representations of local independence
Søren Wengel Mogensen
Graphical models use graphs to represent conditional independence structure in the distribution of a random vector. In stochastic processes, graphs may represent so-called local in…
Time-dependent mediators in survival analysis: Graphical representation of causal assumptions
Søren Wengel Mogensen, Odd O. Aalen, Susanne Strohmaier
We study time-dependent mediators in survival analysis using a treatment separation approach due to Didelez [2019] and based on earlier work by Robins and Richardson [2011]. This a…
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
Alessandro Bregoli, Karin Rathsman, Marco Scutari +2
Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is importa…
Weak equivalence of local independence graphs
Søren Wengel Mogensen
Classical graphical modeling of multivariate random vectors uses graphs to encode conditional independence. In graphical modeling of multivariate stochastic processes, graphs may e…
Instrumental Processes Using Integrated Covariances
Søren Wengel Mogensen
Instrumental variable methods are often used for parameter estimation in the presence of confounding. They can also be applied in stochastic processes. Instrumental variable analys…
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee +6
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i.i.d. observations. Instead, these fields consider th…
Graphical modeling of stochastic processes driven by correlated errors
Søren Wengel Mogensen, Niels Richard Hansen
We study a class of graphs that represent local independence structures in stochastic processes allowing for correlated error processes. Several graphs may encode the same local in…
Causal screening for dynamical systems
Søren Wengel Mogensen
Many classical algorithms output graphical representations of causal structures by testing conditional independence among a set of random variables. In dynamical systems, local ind…
Markov equivalence of marginalized local independence graphs
Søren Wengel Mogensen, Niels Richard Hansen
Symmetric independence relations are often studied using graphical representations. Ancestral graphs or acyclic directed mixed graphs with -separation provide classes of symmetr…