257 citations · 1k across the 41 of their papers we have counts for
7 papers · 1 filter
Sparse Nested Markov models with Log-linear Parameters
Ilya Shpitser, Robin J. Evans, Thomas S. Richardson +1
Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in stati…
Causal Inference in the Presence of Latent Variables and Selection Bias
Peter L. Spirtes, Christopher Meek, Thomas S. Richardson
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…
A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models
Thomas S. Richardson
Although the concept of d-separation was originally defined for directed acyclic graphs (see Pearl 1988), there is a natural extension of he concept to directed cyclic graphs. When…
A Discovery Algorithm for Directed Cyclic Graphs
Thomas S. Richardson
Directed acyclic graphs have been used fruitfully to represent causal strucures (Pearl 1988). However, in the social sciences and elsewhere models are often used which correspond b…
General theory for interactions in sufficient cause models with dichotomous exposures
Tyler J. VanderWeele, Thomas S. Richardson
The sufficient-component cause framework assumes the existence of sets of sufficient causes that bring about an event. For a binary outcome and an arbitrary number of binary causes…
Cross-covariance modelling via DAGs with hidden variables
Jacob A. Wegelin, Thomas S. Richardson
DAG models with hidden variables present many difficulties that are not present when all nodes are observed. In particular, fully observed DAG models are identified and correspond…