Confounder Selection

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).

Confounder selection via iterative graph expansion

Confounder selection via iterative graph expansion

Confounder selection via iterative graph expansion

We propose a systematic way to select confounders

Confounder selection: Objectives and approaches

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