10 papers
Detecting Changes in Causal Dependence with Kernels and Copulas
Shakeel Gavioli-Akilagun, Kieran Wood, Francesco Quinzan
We propose a framework for determining whether the causal dependence of an outcome on a covariate changes at a given time point, given confounders . For ins…
When does Subgroup Balance Matter? Predicting Performance Sensitivity from Latent Separability
Anissa Alloula, Charles Jones, Zuzanna Wakefield-Skorniewska +2
Unequal representation of demographic groups in training data poses challenges to model generalisation across populations. Standard practice assumes that balancing subgroup represe…
Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond
Daqian Shao, Ashkan Soleymani, Francesco Quinzan +1
Solving conditional moment restrictions (CMRs) is a key problem considered in statistics, causal inference, and econometrics, where the aim is to solve for a function of interest t…
AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysis
Haroui Ma, Francesco Quinzan, Theresa Willem +1
Machine learning (ML) systems for medical imaging have demonstrated remarkable diagnostic capabilities, but their susceptibility to biases poses significant risks, since biases may…
Learning Decision Policies with Instrumental Variables through Double Machine Learning
Daqian Shao, Ashkan Soleymani, Francesco Quinzan +1
A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental v…
Diffusion Based Causal Representation Learning
Amir Mohammad Karimi Mamaghan, Andrea Dittadi, Stefan Bauer +2
Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause-effect estimation and the ident…
Double Machine Learning Based Structure Identification from Temporal Data
Emmanouil Angelis, Francesco Quinzan, Ashkan Soleymani +2
Learning the causes of time-series data is a fundamental task in many applications, spanning from finance to earth sciences or bio-medical applications. Common approaches for this…
DRCFS: Doubly Robust Causal Feature Selection
Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet +2
Knowing the features of a complex system that are highly relevant to a particular target variable is of fundamental interest in many areas of science. Existing approaches are often…
Optimal Transport for Correctional Learning
Rebecka Winqvist, Inês Lourenco, Francesco Quinzan +2
The contribution of this paper is a generalized formulation of correctional learning using optimal transport, which is about how to optimally transport one mass distribution to ano…
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…