Thomas S Richardson

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…

Assumptions and Bounds in the Instrumental Variable Model
Thomas S. Richardson, James M. Robins
In this note we give proofs for results relating to the Instrumental Variable (IV) model with binary response $Y$ and binary treatment $X$, but with an…

Individual Treatment Effect: Prediction Intervals and Sharp Bounds
Zhehao Zhang, Thomas S. Richardson
Individual treatment effect (ITE) is often regarded as the ideal target of inference in causal analyses and has been the focus of several recent studies. In…

Marginal log-linear parameters for graphical Markov models
Robin J. Evans, Thomas S. Richardson
Marginal log-linear (MLL) models provide a flexible approach to multivariate discrete data. MLL parametrizations under linear constraints induce a wide variety…

Computing maximum likelihood estimates in recursive linear models with correlated errors
Mathias Drton, Michael Eichler, Thomas S. Richardson
In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by a recursive (or acyclic)…