Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables
This work addresses a fundamental challenge in causal inference for decision-making under uncertainty, offering a theoretical advance with potential applications in domains like healthcare or robotics.
The paper tackles the problem of predicting the effects of sequential plans in systems with hidden variables, establishing a graphical criterion for when such predictions can be made from passive observations and providing a closed-form expression for the probability of achieving a goal.
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.