322 citations · 915 across the 42 of their papers we have counts for
32 papers · 1 filter
Symmetry-Informed Causal Partial Identification
Uzair Akbar, Zulfiqar Zaidi, Niki Kilbertus +2
Partial identification (PI) entails estimating bounds on causal effects by encoding different assumptions on data generation as a constrained optimization problem. Such bounds can…
OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems
Till Richter, Niki Kilbertus
Dynamical systems are fundamental to modeling the natural world, yet modeling them involves a persistent trade-off: manually prescribed mechanistic models are interpretable by desi…
Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations
Birgit Kühbacher, Daan Crommelin, Niki Kilbertus
Weather and climate forecasts are inherently uncertain due to chaotic dynamics, imperfect initial conditions, and incomplete representation of the underlying physical processes. Op…
Limits of Learning Linear Dynamics from Experiments
Aybüke Ulusarslan, Niki Kilbertus, Nora Schneider
Learning governing dynamics from data is a common goal across the sciences, yet it is only well-posed when the underlying mechanisms are identifiable. In practice, many data-driven…
Debiased neural operators for estimating functionals
Konstantin Hess, Dennis Frauen, Niki Kilbertus +1
Neural operators are widely used to approximate solution maps of complex physical systems. In many applications, however, the goal is not to recover the full solution trajectory, b…
Conservative Continuous-Time Treatment Optimization
Nora Schneider, Georg Manten, Niki Kilbertus
We develop a conservative continuous-time stochastic control framework for treatment optimization from irregularly sampled patient trajectories. The unknown patient dynamics are mo…
Graph Distance Based on Cause-Effect Estimands with Latents
Zhufeng Li, Niki Kilbertus
Causal discovery aims to recover graphs that represent causal relations among given variables from observations, and new methods are constantly being proposed. Increasingly, the co…
Predicting symbolic ODEs from multiple trajectories
Yakup Emre Şahin, Niki Kilbertus, Sören Becker
We introduce MIO, a transformer-based model for inferring symbolic ordinary differential equations (ODEs) from multiple observed trajectories of a dynamical system. By combining mu…
An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
Uzair Akbar, Niki Kilbertus, Hao Shen +2
The technique of data augmentation (DA) is often used in machine learning for regularization purposes to better generalize under i.i.d. settings. In this work, we present a unifyin…
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…
Generative Intervention Models for Causal Perturbation Modeling
Nora Schneider, Lars Lorch, Niki Kilbertus +2
We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external…
Learning Representations of Instruments for Partial Identification of Treatment Effects
Jonas Schweisthal, Dennis Frauen, Maresa Schröder +3
Reliable estimation of treatment effects from observational data is important in many disciplines such as medicine. However, estimation is challenging when unconfoundedness as a st…
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…
Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk +7
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of…
Projected Neural Differential Equations for Learning Constrained Dynamics
Alistair White, Anna Büttner, Maximilian Gelbrecht +4
Neural differential equations offer a powerful approach for learning dynamical systems from data. However, they do not inherently respect known constraints, such as conservation la…
Towards Physically Consistent Deep Learning For Climate Model Parameterizations
Birgit Kühbacher, Fernando Iglesias-Suarez, Niki Kilbertus +1
Climate models play a critical role in understanding and projecting climate change. Due to their complexity, their horizontal resolution of about 40-100 km remains too coarse to re…
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…
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
Stéphane d'Ascoli, Sören Becker, Alexander Mathis +2
We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution…
Predicting Ordinary Differential Equations with Transformers
Sören Becker, Michal Klein, Alexander Neitz +2
We develop a transformer-based sequence-to-sequence model that recovers scalar ordinary differential equations (ODEs) in symbolic form from irregularly sampled and noisy observatio…
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
Alistair White, Niki Kilbertus, Maximilian Gelbrecht +1
Many successful methods to learn dynamical systems from data have recently been introduced. However, ensuring that the inferred dynamics preserve known constraints, such as conserv…
Discovering ordinary differential equations that govern time-series
Sören Becker, Michal Klein, Alexander Neitz +2
Natural laws are often described through differential equations yet finding a differential equation that describes the governing law underlying observed data is a challenging and s…
Sparsity in Continuous-Depth Neural Networks
Hananeh Aliee, Till Richter, Mikhail Solonin +3
Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories. While different typ…
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…