• Publications
  • Influence
Ancestral graph Markov models
Abstract : This paper introduces a class of graphical independence models that is closed under marginalization and conditioning but that contains all DAG independence models. This class of graphs,Expand
Markov Properties for Acyclic Directed Mixed Graphs
We consider acycfic directed mixed graphs, in which directed edges (x->y) and bi-directed edges (x 4-+ y) may occur. A simple extension of Pearl's d-separation criterion, called m-separation, isExpand
Learning high-dimensional directed acyclic graphs with latent and selection variables
This work proposes the new RFCI algorithm, which is much faster than FCI, and proves consistency of FCI and RFCI in sparse high-dimensional settings, and demonstrates in simulations that the estimation performances of the algorithms are very similar. Expand
A Discovery Algorithm for Directed Cyclic Graphs
This paper presents a discovery algorithm that is correct in the large sample limit, given commonly (but often implicitly) made plausible assumptions, and which provides information about the existence or non-existence of causal pathways from one variable to another. Expand
Single World Intervention Graphs ( SWIGs ) : A Unification of the Counterfactual and Graphical Approaches to Causality
We present a simple graphical theory unifying causal directed acyclic graphs (DAGs) and potential (aka counterfactual) outcomes via a node-splitting transformation. We introduce a new graph, theExpand
Causal Inference in the Presence of Latent Variables and Selection Bias
We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias may be at work.Expand
Chain graph models and their causal interpretations
Chain graphs are a natural generalization of directed acyclic graphs and undirected graphs. However, the apparent simplicity of chain graphs belies the subtlety of the conditional independenceExpand
Alternative Graphical Causal Models and the Identification of Direct E!ects
We consider four classes of graphical causal models: the Finest Fully Randomized Causally Interpretable Structured Tree Graph (FFRCISTG) of Robins (1986), the agnostic causal model of Spirtes et al.Expand
The TETRAD Project: Constraint Based Aids to Causal Model Specification.
This work begins by drawing the analogy between parameter estimation and model specification search, and describes how the specification of a structural equation model entails familiar constraints on the covariance matrix for all admissible values of its parameters. Expand
Estimation of a covariance matrix with zeros
We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call iterativeExpand