Structure learning in graphical modeling
M Drton, MH Maathuis - Annual Review of Statistics and Its …, 2017 - annualreviews.org
A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …
correspond to variables of interest. The edges of the graph reflect allowed conditional …
Causal feature learning: an overview
K Chalupka, F Eberhardt, P Perona - Behaviormetrika, 2017 - Springer
Causal feature learning (CFL)(Chalupka et al., Proceedings of the Thirty-First Conference on
Uncertainty in Artificial Intelligence. AUAI Press, Edinburgh, pp 181–190, 2015) is a causal …
Uncertainty in Artificial Intelligence. AUAI Press, Edinburgh, pp 181–190, 2015) is a causal …
[PDF][PDF] Constraint-based causal discovery from multiple interventions over overlapping variable sets
S Triantafillou, I Tsamardinos - The Journal of Machine Learning Research, 2015 - jmlr.org
Scientific practice typically involves repeatedly studying a system, each time trying to unravel
a different perspective. In each study, the scientist may take measurements under different …
a different perspective. In each study, the scientist may take measurements under different …
[PDF][PDF] Experiment selection for causal discovery
A Hyttinen, F Eberhardt, PO Hoyer - The Journal of Machine Learning …, 2013 - jmlr.org
Randomized controlled experiments are often described as the most reliable tool available
to scientists for discovering causal relationships among quantities of interest. However, it is …
to scientists for discovering causal relationships among quantities of interest. However, it is …
Discovering cyclic causal models with latent variables: A general SAT-based procedure
A Hyttinen, PO Hoyer, F Eberhardt… - arXiv preprint arXiv …, 2013 - arxiv.org
We present a very general approach to learning the structure of causal models based on d-
separation constraints, obtained from any given set of overlapping passive observational or …
separation constraints, obtained from any given set of overlapping passive observational or …
Directed cyclic graph for causal discovery from multivariate functional data
Discovering causal relationship using multivariate functional data has received a significant
amount of attention very recently. In this article, we introduce a functional linear structural …
amount of attention very recently. In this article, we introduce a functional linear structural …
Intervention design for causal representation learning
In this paper, we take a first step towards bringing two fields of causality closer together:
intervention design and causal representation learning. Intervention design is a well studied …
intervention design and causal representation learning. Intervention design is a well studied …
Causal discovery from multiple data sets with non-identical variable sets
A number of approaches to causal discovery assume that there are no hidden confounders
and are designed to learn a fixed causal model from a single data set. Over the last decade …
and are designed to learn a fixed causal model from a single data set. Over the last decade …
Probabilistic computational causal discovery for systems biology
Discovering the causal mechanisms of biological systems is necessary to design new drugs
and therapies. Computational Causal Discovery (CD) is a field that offers the potential to …
and therapies. Computational Causal Discovery (CD) is a field that offers the potential to …
Causal effect identification from multiple incomplete data sources: A general search-based approach
Causal effect identification considers whether an interventional probability distribution can
be uniquely determined without parametric assumptions from measured source distributions …
be uniquely determined without parametric assumptions from measured source distributions …