Learning to induce causal structure
The fundamental challenge in causal induction is to infer the underlying graph structure
given observational and/or interventional data. Most existing causal induction algorithms …
given observational and/or interventional data. Most existing causal induction algorithms …
Xinsight: explainable data analysis through the lens of causality
In light of the growing popularity of Exploratory Data Analysis (EDA), understanding the
underlying causes of the knowledge acquired by EDA is crucial. However, it remains under …
underlying causes of the knowledge acquired by EDA is crucial. However, it remains under …
Distributionally robust skeleton learning of discrete Bayesian networks
We consider the problem of learning the exact skeleton of general discrete Bayesian
networks from potentially corrupted data. Building on distributionally robust optimization and …
networks from potentially corrupted data. Building on distributionally robust optimization and …
CESAM–Code for European severe accident management, EURATOM project on ASTEC improvement
H Nowack, P Chatelard, L Chailan, V Sanchez… - Annals of Nuclear …, 2018 - Elsevier
Abstract The CESAM FP7 project (Van Dorsselaere et al., 2015) of EURATOM has been
conducted from April 2013 until March 2017 in the aftermath of the Fukushima Dai-ichi …
conducted from April 2013 until March 2017 in the aftermath of the Fukushima Dai-ichi …
Causal discovery in linear structural causal models with deterministic relations
Linear structural causal models (SCMs)–in which each observed variable is generated by a
subset of the other observed variables as well as a subset of the exogenous sources–are …
subset of the other observed variables as well as a subset of the exogenous sources–are …
Reliable and efficient anytime skeleton learning
Skeleton Learning (SL) is the task for learning an undirected graph from the input data that
captures their dependency relations. SL plays a pivotal role in causal learning and has …
captures their dependency relations. SL plays a pivotal role in causal learning and has …
Causal learning with sufficient statistics: an information bottleneck approach
The inference of causal relationships using observational data from partially observed
multivariate systems with hidden variables is a fundamental question in many scientific …
multivariate systems with hidden variables is a fundamental question in many scientific …
Temporal causal modelling on large volume enterprise data
R Mohan, S Chaudhury, B Lall - IEEE Transactions on Big Data, 2021 - ieeexplore.ieee.org
Structural causal modelling (SCM) with its intervention analysis is one of the promising
modelling approach that assists in data driven decision making. SCM not only overcomes …
modelling approach that assists in data driven decision making. SCM not only overcomes …
A Full DAG Score-Based Algorithm for Learning Causal Bayesian Networks with Latent Confounders
C Gonzales, AH Valizadeh - arXiv preprint arXiv:2408.11181, 2024 - arxiv.org
Causal Bayesian networks (CBN) are popular graphical probabilistic models that encode
causal relations among variables. Learning their graphical structure from observational data …
causal relations among variables. Learning their graphical structure from observational data …
Fast bayesian network structure learning using quasi-determinism screening
Learning the structure of Bayesian networks from data is a NP-Hard problem that involves
optimization over a super-exponential sized space. In this work, we show that in most real …
optimization over a super-exponential sized space. In this work, we show that in most real …