55 citations · 257 across the 13 of their papers we have counts for
13 papers
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt +1
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…
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Much of scientific data is collected as randomized experiments intervening on some and observing other variables of interest. Quite often, a given phenomenon is investigated in sev…
Estimating a Causal Order among Groups of Variables in Linear Models
Doris Entner, Patrik O. Hoyer
The machine learning community has recently devoted much attention to the problem of inferring causal relationships from statistical data. Most of this work has focused on uncoveri…
Discovery of non-gaussian linear causal models using ICA
Shohei Shimizu, Aapo Hyvarinen, Yutaka Kano +1
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data (Spirtes et al. 2000; Pearl 2000). Such methods make various as…
Discovering Cyclic Causal Models by Independent Components Analysis
Gustavo Lacerda, Peter L. Spirtes, Joseph Ramsey +1
We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-…
Causal discovery of linear acyclic models with arbitrary distributions
Patrik O. Hoyer, Aapo Hyvarinen, Richard Scheines +4
An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used t…