Graphical Models

On statistical and causal models associated with acyclic directed mixed graphs

A talk that goes over [this paper](publication/admg-model/)

A graphical approach to state variable selection in off-policy learning

We give graphical criteria for state variables to be 'valid' in off-policy learning in a framework that generalizes dynamic treatment regimes (DTRs) and Markov decision processes (MDPs).

On statistical and causal models associated with acyclic directed mixed graphs

Causal models in statistics are often described by acyclic directed mixed graphs (ADMGs), which contain directed and bidirected edges and no directed cycles. This article surveys various interpretations of ADMGs, discusses their relations in …

A matrix algebra for graphical statistical models

Directed mixed graphs permit directed and bidirected edges between any two vertices. They were first considered in the path analysis developed by Sewall Wright and play an essential role in statistical modeling. We introduce a matrix algebra for …

Confounder selection: Objectives and approaches

We provide a unified review of various confounder selection criteria in the literature and the assumptions behind them.