5 citations · 9 across the 7 of their papers we have counts for
8 papers
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Christophe Muller, Ayub Kharel, Alex Luedtke +7
Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy va…
Anti-causal domain generalization: Leveraging unlabeled data
Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller +3
The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing metho…
Sanity Checking Causal Representation Learning on a Simple Real-World System
Juan L. Gamella, Simon Bing, Jakob Runge
We evaluate methods for causal representation learning (CRL) on a simple, real-world system that satisfies the basic problem setup of CRL. The system consists of a controlled optic…
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Antoine Wehenkel, Juan L. Gamella, Ozan Sener +4
Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However,…
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
Juan L. Gamella, Jonas Peters, Peter Bühlmann
In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers m…
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml +1
We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment a…
Learning and scoring Gaussian latent variable causal models with unknown additive interventions
Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml +1
With observational data alone, causal structure learning is a challenging problem. The task becomes easier when having access to data collected from perturbations of the underlying…
Active Invariant Causal Prediction: Experiment Selection through Stability
Juan L. Gamella, Christina Heinze-Deml
A fundamental difficulty of causal learning is that causal models can generally not be fully identified based on observational data only. Interventional data, that is, data origina…