Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the ...
One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...
Functional magnetic resonance imaging (fMRI) in mouse brain, paired with spatially and temporally defined manipulations, offers a powerful tool to causally explain the effect of specific neuronal activity on brain network dynamics. Here, we present an opti ...
Human nutrition and dietary habits shape our health, daily life, societies, the environment, and life on earth in general. However, it remains challenging to understand and attempt to change dietary behaviors using traditional methods due to measurement an ...
This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal disc ...
This thesis consists of three applications of machine learning techniques to risk management. The first chapter proposes a deep learning approach to estimate physical forward default intensities of companies. Default probabilities are computed using artifi ...
Observational studies reporting on adjusted associations between childhood body mass index (BMI; weight (kg)/height (m)(2)) rebound and subsequent cardiometabolic outcomes have often not paid explicit attention to causal inference, including definition of ...
