Causal Inference

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model JMLR 2011 Energetically Optimal Action Potentials NeurIPS 2011 Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables AISTATS 2011 Minimax Algorithm for Learning Rotations COLT 2011 Mixed Cumulative Distribution Networks AISTATS 2011 On Causal Discovery with Cyclic Additive Noise Models NeurIPS 2011 Bayesian structure discovery in Bayesian networks with less space AISTATS 2010 Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference NeurIPS 2010 Causal discovery in multiple models from different experiments NeurIPS 2010 Combining Experiments to Discover Linear Cyclic Models with Latent Variables AISTATS 2010 Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity JMLR 2010 Feature Construction for Inverse Reinforcement Learning NeurIPS 2010 Graphical Gaussian modelling of multivariate time series with latent variables AISTATS 2010 Identifying Cause and Effect on Discrete Data using Additive Noise Models AISTATS 2010 Learning Causal Structure from Overlapping Variable Sets AISTATS 2010 Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation JMLR 2010 Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions JMLR 2010 Neural conditional random fields AISTATS 2010 Probabilistic latent variable models for distinguishing between cause and effect NeurIPS 2010 Sparse Instrumental Variables (SPIV) for Genome-Wide Studies NeurIPS 2010 Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs AISTATS 2010 Abstraction and Relational learning NeurIPS 2009 Bayesian Belief Polarization NeurIPS 2009 Bayesian Network Structure Learning by Recursive Autonomy Identification JMLR 2009 Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm JMLR 2009