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13
A Theory Of Inferred Causation
, 1991
"... This paper concerns the empirical basis of causation, and addresses the following issues: 1. the clues that might prompt people to perceive causal relationships in uncontrolled observations. 2. the task of inferring causal models from these clues, and 3. whether the models inferred tell us anything ..."
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Cited by 215 (35 self)
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This paper concerns the empirical basis of causation, and addresses the following issues: 1. the clues that might prompt people to perceive causal relationships in uncontrolled observations. 2. the task of inferring causal models from these clues, and 3. whether the models inferred tell us anything useful about the causal mechanisms that underly the observations. We propose a minimalmodel semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. We provide an effective algorithm for inferred causation and show that, for a large class of data the algorithm can uncover the direction of causal influences as defined above. Finally, we address the issue of nontemporal causation.
Causal independence for knowledge acquisition and inference. Also in this proceedings
, 1993
"... I introduce a temporal beliefnetwork representation of causal independence that a knowledge engineer can use to elicit probabilistic models. Like the current, atemporal beliefnetwork representation of causal independence, the new representation makes knowledge acquisition tractable. Unlike the ate ..."
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Cited by 43 (4 self)
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I introduce a temporal beliefnetwork representation of causal independence that a knowledge engineer can use to elicit probabilistic models. Like the current, atemporal beliefnetwork representation of causal independence, the new representation makes knowledge acquisition tractable. Unlike the atemproal representation, however, the temporal representation can simplify inference, and does not require the use of unobservable variables. The representation is less general than is the atemporal representation, but appears to be useful for many practical applications. 1
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 39 (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...
Towards normative expert systems: part II, probabilitybased representations for efficient knowledge acquisition and inference. Methods of Information in medicine
 Methods of Information in Medicine
, 1992
"... We address practical issues concerning the construction and use of decisiontheoretic or normative expert systems for diagnosis. In particular, we examine Pathfinder, a normative expert system that assists surgical pathologists with the diagnosis of lymphnode diseases, and discuss the representation ..."
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Cited by 31 (0 self)
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We address practical issues concerning the construction and use of decisiontheoretic or normative expert systems for diagnosis. In particular, we examine Pathfinder, a normative expert system that assists surgical pathologists with the diagnosis of lymphnode diseases, and discuss the representation of dependencies among pieces of evidence within this system. We describe the belief network, a graphical representation of probabilistic dependencies. We see how Pathfinder uses a belief network to construct differential diagnoses efficiently, even when there are dependencies among pieces of evidence. In addition, we introduce an extension of the beliefnetwork representation called a similarity network, a tool for constructing large and complex belief networks. The representation allows a user to construct independent belief networks for subsets of a given domain. A valid belief network for the entire domain can then be constructed from the individual belief networks. We also introduce the partition, a graphical representation that facilitates the assessment of probabilities associated with a belief network. Finally, we show that the similaritynetwork and partition representations made practical the construction of Pathfinder.
From certainty factors to belief networks
 Artificial Intelligence in Medicine 4:35–52
, 1992
"... The certaintyfactor (CF) model is a commonly used method for managing uncertainty in rulebased systems. We review the history and mechanics of the CF model, and delineate precisely its theoretical and practical limitations. In addition, we examine the belief network, a representation that is simil ..."
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The certaintyfactor (CF) model is a commonly used method for managing uncertainty in rulebased systems. We review the history and mechanics of the CF model, and delineate precisely its theoretical and practical limitations. In addition, we examine the belief network, a representation that is similar to the CF model but that is grounded firmly in probability theory. We show that the beliefnetwork representation overcomes many of the limitations of the CF model, and provides a promising approach to the practical construction of expert systems.
Identifying Hidden Variables from ContextSpecific Independencies
 472–477, proceedings of FLAIRS07 Conference
, 2007
"... Learning a Bayesian network from data is a model specific task, and thus requires careful consideration of contextual information, namely, contextual independencies. In this paper, we study the role of hidden variables in learning causal models from data. We show how statistical methods can help us ..."
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Learning a Bayesian network from data is a model specific task, and thus requires careful consideration of contextual information, namely, contextual independencies. In this paper, we study the role of hidden variables in learning causal models from data. We show how statistical methods can help us discover these hidden variables. We suggest hidden variables are wrongly ignored in inference, because they are contextspecific. We show that contextual consideration can help us learn more about true causal relationships hidden in the data. We present a method for correcting models by finding hidden contextual variables, as well as a means for refinining the current, incomplete model.
Exploiting Causal Independence Using Weighted Model Counting
"... Previous studies have demonstrated that encoding a Bayesian network into a SATCNF formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference in Bayesian networks. In this paper, we present techniques for improving this ap ..."
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Previous studies have demonstrated that encoding a Bayesian network into a SATCNF formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference in Bayesian networks. In this paper, we present techniques for improving this approach for Bayesian networks with noisyOR and noisyMAX relations—two relations which are widely used in practice as they can dramatically reduce the number of probabilities one needs to specify. In particular, we present two space efficient CNF encodings for noisyOR/MAX and explore alternative search ordering heuristics. We experimentally evaluated our techniques on largescale real and randomly generated Bayesian networks. On these benchmarks, our techniques gave speedups of up to two orders of magnitude over the best previous approaches and scaled up to networks with larger numbers of random variables.
Probabilities
"... This paper deals with the problem of estimating the probability of causation, that is, the probability that one event was the real cause of another, in a given scenario. Starting from structuralsemantical definitions of the probabilities of necessary or sufficient causation (or both), we show how t ..."
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This paper deals with the problem of estimating the probability of causation, that is, the probability that one event was the real cause of another, in a given scenario. Starting from structuralsemantical definitions of the probabilities of necessary or sufficient causation (or both), we show how to bound these quantities from data obtained in experimental and observational studies, under general assumptions concerning the datagenerating process. In particular, we strengthen the results of Pearl [39] by presenting sharp bounds based on combined experimental and nonexperimental data under no process assumptions, as well as under the mild assumptions of exogeneity (no confounding) and monotonicity (no prevention). These results delineate more precisely the basic assumptions that must be made before statistical measures such as the excessriskratio could be used for assessing attributional quantities such as the probability of causation. 1.
PROBABILISTIC ANALYSES AND THE HUMEAN CONCEPTION OF THE RELATIONSHIP BETWEEN LEVELS OF CAUSALITY 1
"... It is now usual to distinguish between different causal relations. The distinction I shall be more specifically interested in here is between generic causality and singular causality. Generic causality is the relation one refers to in (e.g.) "Exposure to asbestos causes cancer " whereas si ..."
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It is now usual to distinguish between different causal relations. The distinction I shall be more specifically interested in here is between generic causality and singular causality. Generic causality is the relation one refers to in (e.g.) "Exposure to asbestos causes cancer " whereas singular causality is the relation one refers to in (e.g.) "Peter's being exposed to asbestos caused him to develop
Does a Cause Increase the Probability of Its Effects?
, 1999
"... I defend the intuition that a cause raises the probability of its eects, by formulating a dependence principle which expresses this intuition and holds up well against counterexamples in the philosophical literature. There are several options for an account of causality, each of which is based on ..."
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I defend the intuition that a cause raises the probability of its eects, by formulating a dependence principle which expresses this intuition and holds up well against counterexamples in the philosophical literature. There are several options for an account of causality, each of which is based on an intuition about causality. For instance: mechanistic account: this is based on the intuition that there is a physical process linking cause and eect; probabilistic: the intuition is that a cause makes its eects more likely; counterfactual: if a cause were to occur then so would its eects; strategic: bringing about a cause is a good way to bring about its eects. explanatory: a cause is a good explanation for its eects. Ideally, an account of causality would reconcile all these intuitions, but this task has so far proved notoriously dicult. One prime problem is a conict between the mechanistic and the probabilistic accounts  there are several apparent examples in th...