structural equation model

Estimating Causal Effects with Ancestral Graph Markov Models PGM 2016 Identification and Overidentification of Linear Structural Equation Models NeurIPS 2016 Learning Acyclic Directed Mixed Graphs from Observations and Interventions PGM 2016 Learning Causal Graphs with Small Interventions NeurIPS 2015 Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-specific Confounder Variables and Non-Gaussian Distributions JMLR 2014 Causal Discovery with Continuous Additive Noise Models JMLR 2014 Causal Inference on Time Series using Restricted Structural Equation Models NeurIPS 2013 Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models JMLR 2013 SADA: A General Framework to Support Robust Causation Discovery ICML 2013 Causal discovery with scale-mixture model for spatiotemporal variance dependencies NeurIPS 2012 DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model JMLR 2011 Structural equations and divisive normalization for energy-dependent component analysis NeurIPS 2011 Combining Experiments to Discover Linear Cyclic Models with Latent Variables AISTATS 2010 Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs AISTATS 2010 Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors JMLR 2009 Markov Properties for Linear Causal Models with Correlated Errors JMLR 2009 Nonlinear causal discovery with additive noise models NeurIPS 2008 A Linear Non-Gaussian Acyclic Model for Causal Discovery JMLR 2006 Learning Structural Equation Models for fMRI NeurIPS 2006