Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables

TL;DR

This paper introduces a graphical criterion to evaluate the success probability of plans involving multiple actions in causal models with hidden variables, enabling predictions from passive observations.

Contribution

It provides a novel graphical criterion and a closed-form expression for probabilistic plan evaluation in causal models with unmeasured variables.

Findings

Graphical criterion for plan effect predictability

Closed-form expression for success probability

Applicable to plans with concurrent or sequential actions

Abstract

The paper concerns the probabilistic evaluation of plans in the presence of unmeasured variables, each plan consisting of several concurrent or sequential actions. We establish a graphical criterion for recognizing when the effects of a given plan can be predicted from passive observations on measured variables only. When the criterion is satisfied, a closed-form expression is provided for the probability that the plan will achieve a specified goal.