Identifiability of causal effects in a multi-agent causal model (2003) [4 citations — 2 self]
Abstract:
In this paper we introduce multi-agent causal models (MACMs) 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 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 purely observational data. More specifically, we extend an existing single agent identification algorithm to 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 informati on concerning variables that are shared by agents A and B and variables that are being studied in that specific query. KEY WORDS causal models, Bayesian networks, multi-agent modeling, identification 1
Citations
| 4388 | Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference – Pearl - 1988 |
| 345 | Causality: Models, reasoning, and inference – Pearl - 2000 |
| 55 | Multiply sectioned Bayesian networks and junction forests for large knowledge based systems – Xiang, Poole, et al. - 1993 |
| 30 | Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach – Xiang - 2002 |
| 17 | A general identification condition for causal effects – Tian, Pearl |
| 10 | On the identification of causal effects – Tian, Pearl - 2002 |
| 5 | Studies in causal reasoning and learning – Tian - 2002 |
| 1 | Models, Reasoning and Inference – Causality - 2000 |
