7 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…
Representation Invariance and Allocation: When Subgroup Balance Matters
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 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…
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 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…