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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 16 (10 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
A causal theory of counterfactuals
 Nous
, 2005
"... I develop an account of counterfactual conditionals using “causal models”, and argue that this account is preferable to the currently standard account in terms of “similarity of possible worlds ” due to David Lewis and Robert Stalnaker. I diagnose the attraction of counterfactual theories of causati ..."
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Cited by 10 (0 self)
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I develop an account of counterfactual conditionals using “causal models”, and argue that this account is preferable to the currently standard account in terms of “similarity of possible worlds ” due to David Lewis and Robert Stalnaker. I diagnose the attraction of counterfactual theories of causation, and argue that it is illusory. 1. Lewis’s theory and some problem cases. 2. Diagnosis. 3. Causal models and counterfactuals.
Probabilities of causation: Bounds and identi cation
 In Proceedings of the Sixteenth Conference on Uncertainty in Arti cial Intelligence
, 2000
"... This paper deals with the problem of estimating the probability that one event was a cause of another in a given scenario. Using structuralsemantical de nitions of the probabilities of necessary or su cient causation (or both), we show howto optimally bound these quantities from data obtained in ex ..."
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Cited by 6 (5 self)
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This paper deals with the problem of estimating the probability that one event was a cause of another in a given scenario. Using structuralsemantical de nitions of the probabilities of necessary or su cient causation (or both), we show howto optimally bound these quantities from data obtained in experimental and observational studies, making minimal assumptions concerning the datagenerating process. In particular, we strengthen the results of Pearl (1999) by weakening the datageneration assumptions and deriving theoretically sharp bounds on the probabilities of causation. These results delineate precisely how empirical data can be used both in settling questions of attribution and in solving attributionrelated problems of decision making. 1
The swine flu vaccine and GuillainBarré syndrome: a case study in relative risk and specific causation
 Evaluation Review
, 1999
"... Epidemiologic methods were developed to prove general causation: identifying exposures that increase the risk of particular diseases. Courts often are more interested in specific causation: on balance of probabilities, was the plainti#'s disease caused by exposure to the agent in quest ..."
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Cited by 5 (1 self)
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<F4.554e+05> Epidemiologic methods were developed to prove general causation: identifying exposures that increase the risk of particular diseases. Courts often are more interested in specific causation: on balance of probabilities, was the plainti#'s disease caused by exposure to the agent in question? Some authorities have suggested that a relative risk greater than 2.0 meets the standard of proof for specific causation. Such a definite criterion is appealing, but there are di#culties. Bias and confounding are familiar problems; individual di#erences must be considered too. The issues are explored in the context of the swine flu vaccine and GuillainBarre syndrome. The conclusion: there is a considerable gap between relative risks and proof of specific causation.<F4.051e+05> 1. Introduction<F4.554e+05> In a toxic tort case, the plainti# is exposed to a toxic agent, su#ers injury, and sues. To win, the plainti# must prove (i) "general causation" (the agent is capable of producing th...
Causal inference based on counterfactuals
 BMC MEDICAL RESEARCH METHODOLOGY
, 2005
"... Background
The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies.
Discussion
This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when ..."
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Cited by 1 (0 self)
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Background
The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies.
Discussion
This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, timevarying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures.
Summary
Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.
Epistemological foundations for the representation of discourse context
, 2004
"... The first four sections of the paper focus on characterizing the elements that enter into the characterization of the notion of discourse context. One way of doing so is by identifying this notion with the set of commonly presupposed items. I propose a multiagent account of context where it is esse ..."
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The first four sections of the paper focus on characterizing the elements that enter into the characterization of the notion of discourse context. One way of doing so is by identifying this notion with the set of commonly presupposed items. I propose a multiagent account of context where it is essential to represent what each agent takes as being commonly presupposed, aside from what is commonly presupposed. The account requires adding dynamic features to context, in terms of the capacities of supposing that something is the case, given a current context; or in terms of the capacity of updating a context with new information. These dynamic features figure prominently in the proposed characterization. The final sections of the paper focus on the inferential role played by the doxastic commitments induced by discourse context. I argue that these commitments do play a crucial role in understanding how agents reason defeasibly from the point of view of a given context. I discuss also some of the existing accounts, in terms of autoepistemic operators. I argue that they cannot provide a good encoding for conversational implicatures of the type Grice studied. The article offers instead an alternative account of autoepistemic inference based on an insight presented by Paul Grice in a recent addendum to his seminal article ‘Logic and Conversation’. 1
Probabilities of Causation: Bounds and
, 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 de nitions of the probabilities of necessary or su cient causation (or both), we show how to b ..."
Abstract
 Add to MetaCart
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 de nitions of the probabilities of necessary or su cient 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