Identification in Chain Multi-Agent Causal Models

Sam Maes, Stijn Meganck, and Bernard Manderick, Vrije Universiteit

In this paper we introduce chain multi-agent causal models which are an extension of causal Bayesian networks to a multi-agent setting. Instead of 1 single agent modeling the entire domain, there are several agents organised in a chain, each modeling non-disjoint subsets of the domain. Every agent has a causal model, determined by an acyclic causal diagram and a joint probability distribution over its observed variables.

We study the identification of causal effects, which is the calculation of the effect of manipulating a variable on other variables from observational data and a causal diagram.

More specifically, we extend an existing single agent identification algorithm to chain multi-agent causal models. Given some assumptions, we provide a technique to calculate the effect of manipulating a variable in agent A on some variables in another agent B, while only communicating information concerning variables that are shared between neighboring agents on the chain between A and B and variables that are being studied in that specific query.


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