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Reasoning With Cause And Effect
, 1999
"... This paper summarizes basic concepts and principles that I have found to be useful in dealing with causal reasoning. The paper is written as a companion to a lecture under the same title, to be presented at IJCAI99, and is intended to supplement the lecture with technical details and pointers to mo ..."
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Cited by 36 (0 self)
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This paper summarizes basic concepts and principles that I have found to be useful in dealing with causal reasoning. The paper is written as a companion to a lecture under the same title, to be presented at IJCAI99, and is intended to supplement the lecture with technical details and pointers to more elaborate discussions in the literature. The ruling conception will be to treat causation as a computational schema devised to identify the invariant relationships in the environment, so as to facilitate reliable prediction of the effect of actions. This conception, as well as several of its satellite principles and tools, has been guiding paradigm for several research communities in AI, most notably those connected with causal discovery, troubleshooting, planning under uncertainty and modeling the behavior of physical systems. My hopes are to encourage a broader and more effective usage of causal modeling by explicating these common principles in simple and familiar mathematical form. Af...
Defining Explanation in Probabilistic Systems
 In Proc. UAI97
, 1997
"... As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to expla ..."
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Cited by 23 (3 self)
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As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to explanation in the literature one due to G ardenfors and one due to Pearland show that both suffer from significant problems. We propose an approach to defining a notion of "better explanation" that combines some of the features of both together with more recent work by Pearl and others on causality. 1 INTRODUCTION Probabilistic inference is often hard for humans to understand. Even a simple inference in a small domain may seem counterintuitive and surprising; the situation only gets worse for large and complex domains. Thus, a system doing probabilistic inference must be able to explain its findings and recommendations to evoke confidence on the part of the user. Indeed, in experiments wi...
Exploiting CaseBased Independence for Approximating Marginal Probabilities
 International Journal of Approximate Reasoning
, 1994
"... Computing marginal probabilities (whether prior or posterior) in Bayesian belief networks is a hard problem. This paper discusses deterministic approximation schemes that work by adding up the probability mass in a small number of value assignments to the network variables. Under certain assumptions ..."
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Cited by 14 (7 self)
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Computing marginal probabilities (whether prior or posterior) in Bayesian belief networks is a hard problem. This paper discusses deterministic approximation schemes that work by adding up the probability mass in a small number of value assignments to the network variables. Under certain assumptions, the probability mass in the union of these assignments is sufficient to obtain a good approximation. Such methods are especially useful for highlyconnected networks, where the maximum clique size or the cutset size make the standard algorithms intractable. In considering assignments, it is not necessary to assign values to variables that are independent of (dseparated from) the evidence and query nodes. In many cases, however, there is a finer independence structure not evident from the topology, but dependent on the conditional distributions of the nodes. We note that independencebased (IB) assignments, which were originally proposed as theory of abductive explanations, take advantage ...
Deterministic Approximation of Marginal Probabilities in Bayes Nets
, 1998
"... Computation of marginal probabilities in Bayes nets is central to numerous reasoning and automatic decisionmaking systems. This paper presents a deterministic approximation scheme for this hard problem, that supplies provably correct bounds, by aggregating probability mass in IndependenceBased (IB ..."
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Cited by 6 (1 self)
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Computation of marginal probabilities in Bayes nets is central to numerous reasoning and automatic decisionmaking systems. This paper presents a deterministic approximation scheme for this hard problem, that supplies provably correct bounds, by aggregating probability mass in IndependenceBased (IB) assignments. The
Explaining Predictions in Bayesian Networks and Influence Diagrams
 In Proc. of the AAAI 1998 Spring Symposium Series: Interactive and MixedInitiative DecisionTheoretic Systems
, 1998
"... As Bayesian Networks and Influence Diagrams are being used more and more widely, the importance of an efficient explanation mechanism becomes more apparent. We focus on predictive explanations, the ones designed to explain predictions and recommendations of probabilistic systems. We analyze the issu ..."
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Cited by 2 (0 self)
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As Bayesian Networks and Influence Diagrams are being used more and more widely, the importance of an efficient explanation mechanism becomes more apparent. We focus on predictive explanations, the ones designed to explain predictions and recommendations of probabilistic systems. We analyze the issues involved in defining, computing and evaluating such explanations and present an algorithm to compute them. Introduction As knowledgebased reasoning systems begin addressing realworld problems, they are often designed to be used not by experts but by people unfamiliar with the domain. Such people are unlikely to accept systems prediction or advice without some explanation. In addition, the systems ever increasing size makes their computations more and more difficult to follow even for their creators. This situation makes an explanation mechanism critical for making these systems useful and widely accepted. Probabilistic systems, such as Bayesian Networks (Pearl 1988) and Influence Di...
Exploiting IB Assignments for Approximating Marginal Probabilities
, 1994
"... Computing marginal probabilities (whether prior or posterior) in Bayesian belief networks is a hard problem. This paper discusses deterministic approximation schemes that work by adding up the probability mass in a small number of value assignments to the network variables. Under certain assumptions ..."
Abstract
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Computing marginal probabilities (whether prior or posterior) in Bayesian belief networks is a hard problem. This paper discusses deterministic approximation schemes that work by adding up the probability mass in a small number of value assignments to the network variables. Under certain assumptions, the probability mass in the union of these assignments is sufficient to obtain a good approximation. Such methods are especially useful for highlyconnected networks, where the maximum clique size or the cutset size make the standard algorithms intractable. In considering assignments, it is not necessary to assign values to variables that are independent of (dseparated from) the evidence and query nodes. In many cases, however, there is a finer independence structure not evident from the topology, but dependent on the conditional distributions of the nodes. We note that independencebased (IB) assignments, which were originally proposed as theory of abductive explanations, take advantage ...