7 papers
: Decision-Targeted Digital Twins
Harry Amad, Mihaela van der Schaar
A digital twin (DT) is a virtual model of a real-world system that can assist decision-making by simulating scenarios induced by different policies. However, typical machine learni…
OncoSynth: Synthetic data generation for treatment effect estimation in oncology
Octavia-Andreea Ciora, Julian Welzel, Dennis Frauen +6
In oncology, access to patient-level data is often restricted. Synthetic data provides an alternative for analyzing treatment effectiveness, but existing methods for synthetic data…
Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport
Harry Amad, Mihaela van der Schaar
Neural networks (NNs) often have critical behavioural trade-offs that are set at design time with hyperparameters-such as reward weights in reinforcement learning or quantile targe…
Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
Harry Amad, Zhaozhi Qian, Dennis Frauen +3
Causal inference is essential for developing and evaluating medical interventions, yet real-world medical datasets are often difficult to access due to regulatory barriers. This ma…
Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time
Julianna Piskorz, Krzysztof Kacprzyk, Harry Amad +1
The Average Treatment Effect (ATE) is a foundational metric in causal inference, widely used to assess intervention efficacy in randomized controlled trials (RCTs). However, in man…
Continuously Updating Digital Twins using Large Language Models
Harry Amad, Nicolás Astorga, Mihaela van der Schaar
Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions. In complex settings, the state and action variables, and available…