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Causal Inference

3310 directly classified papers

Papers per year

Papers

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model JMLR 2011
A rational model of causal inference with continuous causes NIPS 2011
Collective Graphical Models NIPS 2011
On Causal Discovery with Cyclic Additive Noise Models NIPS 2011
Energetically Optimal Action Potentials NIPS 2011
Minimax Algorithm for Learning Rotations COLT 2011
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables AISTATS 2011
Mixed Cumulative Distribution Networks AISTATS 2011
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
Neural conditional random fields AISTATS 2010
Bayesian structure discovery in Bayesian networks with less space AISTATS 2010
Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference NIPS 2010
Causal discovery in multiple models from different experiments NIPS 2010
Feature Construction for Inverse Reinforcement Learning NIPS 2010
Probabilistic latent variable models for distinguishing between cause and effect NIPS 2010
Learning Causal Structure from Overlapping Variable Sets AISTATS 2010
Sparse Instrumental Variables (SPIV) for Genome-Wide Studies NIPS 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
Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs 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
Maximum Entropy Discrimination Markov Networks JMLR 2009
Abstraction and Relational learning NIPS 2009