6 papers
How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Julia Kostin, Kasra Jalaldoust, Elias Bareinboim +2
Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on. Recent work in causality-base…
Adapting, Fast and Slow: On Few-Shot Transportability of Compositions
Kasra Jalaldoust, Elias Bareinboim
Generalization across domains requires stable structure that links the source and target distributions. Building on causal transportability theory, we study a sequential prediction…
Abduction-Deduction Entanglement: Domain Generalization via Representation Transplants
Kasra Jalaldoust, Elias Bareinboum
Prediction models trained under the source distribution do not generalize well to a different target distribution. A valid inference about an unseen data distribution must be ancho…
A Causal Formulation of Spike-Wave Duality
Kasra Jalaldoust, Erfan Zabeh
Understanding the relationship between brain activity and behavior is a central goal of neuroscience. Despite significant advances, a fundamental dichotomy persists: neural activit…
Multi-Domain Causal Discovery in Bijective Causal Models
Kasra Jalaldoust, Saber Salehkaleybar, Negar Kiyavash
We consider the problem of causal discovery (a.k.a., causal structure learning) in a multi-domain setting. We assume that the causal functions are invariant across the domains, whi…
Partial Transportability for Domain Generalization
Kasra Jalaldoust, Alexis Bellot, Elias Bareinboim
A fundamental task in AI is providing performance guarantees for predictions made in unseen domains. In practice, there can be substantial uncertainty about the distribution of new…