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Causes and explanations: A structuralmodel approach
 In Proceedings IJCAI01
, 2001
"... We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions ..."
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Cited by 194 (14 self)
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We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions
Direct and Indirect Effects
, 2005
"... The direct effect of one event on another can be defined and measured by holding constant all intermediate variables between the two. Indirect effects present conceptual and practical difficulties (in nonlinear models), because they cannot be isolated by holding certain variables constant. This pape ..."
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Cited by 134 (27 self)
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The direct effect of one event on another can be defined and measured by holding constant all intermediate variables between the two. Indirect effects present conceptual and practical difficulties (in nonlinear models), because they cannot be isolated by holding certain variables constant. This paper presents a new way of defining the effect transmitted through a restricted set of paths, without controlling variables on the remaining paths. This permits the assessment of a more natural type of direct and indirect effects, one that is applicable in both linear and nonlinear models and that has broader policyrelated interpretations. The paper establishes conditions under which such assessments can be estimated consistently from experimental and nonexperimental data, and thus extends pathanalytic techniques to nonlinear and nonparametric models.
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 40 (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...
Explaining Counterexamples Using Causality
"... Abstract. When a model does not satisfy a given specification, a counterexample is produced by the model checker to demonstrate the failure. A user must then examine the counterexample trace, in order to visually identify the failure that it demonstrates. If the trace is long, or the specification i ..."
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Cited by 27 (1 self)
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Abstract. When a model does not satisfy a given specification, a counterexample is produced by the model checker to demonstrate the failure. A user must then examine the counterexample trace, in order to visually identify the failure that it demonstrates. If the trace is long, or the specification is complex, finding the failure in the trace becomes a nontrivial task. In this paper, we address the problem of analyzing a counterexample trace and highlighting the failure that it demonstrates. Using the notion of causality, introduced by Halpern and Pearl, we formally define a set of causes for the failure of the specification on the given counterexample trace. These causes are marked as red dots and presented to the user as a visual explanation of the failure. We study the complexity of computing the exact set of causes, and provide a polynomialtime algorithm that approximates it. This algorithm is implemented as a feature in the IBM formal verification platform RuleBase PE, where these visual explanations are an integral part of every counterexample trace. Our approach is independent of the tool that produced the counterexample, and can be applied as a lightweight external layer to any model checking tool, or used to explain simulation traces. 1
Defaults and Normality in Causal Structures
"... A serious defect with the HalpernPearl (HP) definition of causality is repaired by combining a theory of causality with a theory of defaults. In addition, it is shown that (despite a claim to the contrary) a cause according to the HP condition need not be a single conjunct. A definition of causalit ..."
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Cited by 21 (8 self)
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A serious defect with the HalpernPearl (HP) definition of causality is repaired by combining a theory of causality with a theory of defaults. In addition, it is shown that (despite a claim to the contrary) a cause according to the HP condition need not be a single conjunct. A definition of causality motivated by Wright’s NESS test is shown to always hold for a single conjunct. Moreover, conditions that hold for all the examples considered by HP are given that guarantee that causality according to (this version) of the NESS test is equivalent to the HP definition. 1
Probabilities of Causation: Bounds and Identification
 Annals of Mathematics and Artificial Intelligence
, 2000
"... 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 h ..."
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Cited by 21 (11 self)
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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 (1999) 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
Probabilities of causation: Three counterfactual interpretations and their identification
 SYNTHESE
, 1999
"... According to common judicial standard, judgment in favor of plaintiff should be made if and only if it is "more probable than not" that the defendant's action was the cause for the plaintiff's damage (or death). This paper provides formal semantics, based on structural models ..."
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Cited by 18 (4 self)
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According to common judicial standard, judgment in favor of plaintiff should be made if and only if it is "more probable than not" that the defendant's action was the cause for the plaintiff's damage (or death). This paper provides formal semantics, based on structural models of counterfactuals, for the probability that event x was a necessary or sufficient cause (or both) of another event y. The paper then explicates conditions under which the probability of necessary (or sufficient) causation can be learned from statistical data, and shows how data from both experimental and nonexperimental studies can be combined to yield information that neither study alone can provide. Finally,weshow that necessity and sufficiency are two independent aspects of causation, and that both should be invoked in the construction of causal explanations for specific scenarios.
Causation: A unified framework for the social sciences
 Journal of Theoretical Politics
, 2005
"... This paper offers four main arguments about the nature of causation in the social sciences. First, contrary to most recent work, I argue that there is a unitary conception of causation: a cause raises the probability of an event. This understanding of causation, borrowed from but not wedded to Bayes ..."
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Cited by 17 (0 self)
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This paper offers four main arguments about the nature of causation in the social sciences. First, contrary to most recent work, I argue that there is a unitary conception of causation: a cause raises the probability of an event. This understanding of causation, borrowed from but not wedded to Bayesian inference, provides common semantic ground on which to base a reconstruction of causation. I argue, second, that rather than thinking about causation as a series of discrete types or distinct rules we ought to reconceptualize this complex form of argument as a set of logical criteria applying to all arguments that are causal in nature (following the foregoing definition), across fields and across methods. Here, it is helpful to distinguish between the formal properties of a causal argument and the methods by which such an argument might be tested, the research design. Sixteen criteria apply to the former and seven criteria apply to the latter, as I show in the body of the paper. In summary, causation in the social sciences is both more diverse and more unified than has generally been recognized.