Causal Inference

Sparse Instrumental Variables (SPIV) for Genome-Wide Studies NeurIPS 2010 Sufficient covariates and linear propensity analysis AISTATS 2010 Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs AISTATS 2010 Bayesian Sparse Factor Models and DAGs Inference and Comparison NeurIPS 2009 Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation JMLR 2009 Nonlinear directed acyclic structure learning with weakly additive noise models NeurIPS 2009 Properties of Monotonic Effects on Directed Acyclic Graphs JMLR 2009 Time-Varying Dynamic Bayesian Networks NeurIPS 2009 Active Learning of Causal Networks with Intervention Experiments and Optimal Designs JMLR 2008 Bounding Performance Loss in Approximate MDP Homomorphisms NeurIPS 2008 Causal Reasoning with Ancestral Graphs JMLR 2008 Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets NeurIPS 2008 Finding Optimal Bayesian Network Given a Super-Structure JMLR 2008 How memory biases affect information transmission: A rational analysis of serial reproduction NeurIPS 2008 Integrating Locally Learned Causal Structures with Overlapping Variables NeurIPS 2008 Nonlinear causal discovery with additive noise models NeurIPS 2008 Using Markov Blankets for Causal Structure Learning JMLR 2008 Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm JMLR 2007 The Noisy-Logical Distribution and its Application to Causal Inference NeurIPS 2007 A Linear Non-Gaussian Acyclic Model for Causal Discovery JMLR 2006 Combining causal and similarity-based reasoning NeurIPS 2006 Learning Structural Equation Models for fMRI NeurIPS 2006