5 papers · 1 filter
Identifiability Challenges in Sparse Linear Ordinary Differential Equations
Cecilia Casolo, Sören Becker, Niki Kilbertus
Dynamical systems modeling is a core pillar of scientific inquiry across natural and life sciences. Increasingly, dynamical system models are learned from data, rendering identifia…
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…
Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series
Thomas Schwarz, Cecilia Casolo, Niki Kilbertus
In personalized medicine, the ability to predict and optimize treatment outcomes across various time frames is essential. Additionally, the ability to select cost-effective treatme…
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…
Learning Counterfactually Invariant Predictors
Francesco Quinzan, Cecilia Casolo, Krikamol Muandet +2
Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world. We propose graphical criteria that yield…