257 citations · 1k across the 41 of their papers we have counts for
8 papers · 1 filter
Potential Outcome and Decision Theoretic Foundations for Statistical Causality
Thomas S. Richardson, James M. Robins
In a recent paper published in the Journal of Causal Inference, Philip Dawid has described a graphical causal model based on decision diagrams. This article describes how single-wo…
Chernoff-type Concentration of Empirical Probabilities in Relative Entropy
F. Richard Guo, Thomas S. Richardson
We study the relative entropy of the empirical probability vector with respect to the true probability vector in multinomial sampling of categories, which, when multiplied by s…
On Testing Marginal versus Conditional Independence
F. Richard Guo, Thomas S. Richardson
We consider testing marginal independence versus conditional independence in a trivariate Gaussian setting. The two models are non-nested and their intersection is a union of two m…
Smooth, identifiable supermodels of discrete DAG models with latent variables
Robin J. Evans, Thomas S. Richardson
We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with latent variables. Such models a…
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…
Markovian acyclic directed mixed graphs for discrete data
Robin J. Evans, Thomas S. Richardson
Acyclic directed mixed graphs (ADMGs) are graphs that contain directed () and bidirected () edges, subject to the constraint that there are no cycles…