Identifying Dynamic Sequential Plans

TL;DR

This paper discusses how to identify dynamic sequential plans within causal Bayesian networks by reducing the problem to causal effect identification, leveraging existing algorithms for complete identification.

Contribution

It introduces a method to identify dynamic sequential plans using causal Bayesian networks, connecting it to established causal effect identification techniques.

Findings

Reduction of dynamic plan identification to causal effect identification

Utilization of existing algorithms for complete identification

Framework applicable to various causal Bayesian network models

Abstract

We address the problem of identifying dynamic sequential plans in the framework of causal Bayesian networks, and show that the problem is reduced to identifying causal effects, for which there are complete identi cation algorithms available in the literature.