In manufacturing, causal relations between components have become crucial to automate
assembly lines. Identifying these relations permits error tracing and correction in the …

Discovering causal relations from observational data is at the heart of scientific research.
Most causal discovery methods assume that the data have only one variable type. In real …

Abstract We present Global Causal Analysis (GCA) for text classification. GCA is a technique
for global model-agnostic explainability drawing from well-established observational causal …

This doctoral thesis delves into the realms of abstractive summarization and causal
discovery within complex systems. I present a set of new methods that counter prevailing …

First, I want to congratulate the authors for this highly interesting paper proposing a new
class of hybrid Bayesian networks and proving that they can be learned from data (both …

This is an interesting paper that distils structure learning in Bayesian networks (BNs) and
kernel methods in a quest to produce more flexible distributional assumptions. Conditional …

A causal graph can be generated from a dataset using a particular causal algorithm, for
instance, the PC algorithm, Fast Causal Inference (FCI) or Really Fast Causal Inference …