Ancestral graph Markov models
A class of graphical independence models that is closed under marginalization and conditioning but that contains all DAG independence models, called maximal ancestral graphs, which lead to a simple parametrization of the corresponding set of distributions in the Gaussian case.
Markov Properties for Acyclic Directed Mixed Graphs
- T. Richardson
- 1 March 2003
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, is…
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.
Single World Intervention Graphs ( SWIGs ) : A Unification of the Counterfactual and Graphical Approaches to Causality
- T. Richardson
- Computer Science
A new graph, the Single-World Intervention Graph (SWIG), which encodes the counterfactual independences associated with a specific hypothetical intervention on the set of treatment variables and the identifying formula is the extended g-computation formula introduced in (Robins et al., 2004).
Causal Inference in the Presence of Latent Variables and Selection Bias
- P. Spirtes, Christopher Meek, T. Richardson
- Psychology, Computer ScienceConference on Uncertainty in Artificial…
- 18 August 1995
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.…
A Discovery Algorithm for Directed Cyclic Graphs
- T. Richardson
- Computer ScienceConference on Uncertainty in Artificial…
- 1 August 1996
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.
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 independence…
Alternative Graphical Causal Models and the Identification of Direct E!ects
This paper analyzes various measures of the ‘direct’ causal effect, focussing on the pure direct effect (PDE), and introduces the Minimal Counterfactual Model (MCM) which is referred to as ‘minimal’ because it imposes the minimal counterfactual independence assumptions.
Binary models for marginal independence
Summary. Log‐linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log‐linear models provides a general framework for modelling…
MARKOV EQUIVALENCE FOR ANCESTRAL GRAPHS
Ancestral graphs can encode conditional independence relations that arise in directed acyclic graph (DAG) models with latent and selection variables. However, for any ancestral graph, there may be…