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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 118 (9 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
Bounds on Treatment Effects from Studies with Imperfect Compliance
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 1997
"... This paper establishes nonparametric formulas that can be used to bound the average treatment effect in experimental studies in which treatment assignment is random but subject compliance is imperfect. The bounds provided are the tightest possible, given the distribution of assignments, treatment ..."
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Cited by 56 (13 self)
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This paper establishes nonparametric formulas that can be used to bound the average treatment effect in experimental studies in which treatment assignment is random but subject compliance is imperfect. The bounds provided are the tightest possible, given the distribution of assignments, treatments, and responses. The formulas show that even with high rates of noncompliance, experimental data can yield useful and sometimes accurate information on the average e#ect of a treatment on the population.
An Axiomatic Characterization of Causal Counterfactuals
, 1998
"... This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedback ..."
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Cited by 47 (19 self)
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This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedbackless) models are considered. Composition and effectiveness also hold in Lewis's closestworld semantics, which implies that for recursive models the causal interpretation imposes no restrictions beyond those embodied in Lewis's framework. A third property, called reversibility, holds in nonrecursive causal models but not in Lewis's closestworld semantics, which implies that Lewis's axioms do not capture some properties of systems with feedback. Causal inferences based on counterfactual analysis are exemplified and compared to those based on graphical models.
Identifiability of pathspecific effects
 In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence IJCAI05
, 2005
"... Counterfactual quantities representing pathspecific effects arise in cases where we are interested in computing the effect of one variable on another only along certain causal paths in the graph (in other words by excluding a set of edges from consideration). A recent paper [7] details a method by ..."
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Cited by 27 (14 self)
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Counterfactual quantities representing pathspecific effects arise in cases where we are interested in computing the effect of one variable on another only along certain causal paths in the graph (in other words by excluding a set of edges from consideration). A recent paper [7] details a method by which such an exclusion can be specified formally by fixing the value of the parent node of each excluded edge. In this paper we derive simple, graphical conditions for experimental identifiability of pathspecific effects, namely, conditions under which pathspecific effects can be estimated consistently from data obtained from controlled experiments. 1
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...
A Clinician's Tool for Analyzing Noncompliance
, 1996
"... We describe a computer program to assist a clinician with assessing the efficacy of treatments in experimental studies for which treatment assignment is random but subject compliance is imperfect. The major difficulty in such studies is that treatment efficacy is not "identifiable", that is, it ..."
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Cited by 20 (11 self)
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We describe a computer program to assist a clinician with assessing the efficacy of treatments in experimental studies for which treatment assignment is random but subject compliance is imperfect. The major difficulty in such studies is that treatment efficacy is not "identifiable", that is, it cannot be estimated from the data, even when the number of subjects is infinite, unless additional knowledge is provided. Our system combines Bayesian learning with Gibbs sampling using two inputs: (1) the investigator's prior probabilities of the relative sizes of subpopulations and (2) the observed data from the experiment. The system outputs a histogram depicting the posterior distribution of the average treatment effect, that is, the probability that the average outcome (e.g., survival) would attain a given level, had the treatment been taken uniformly by the entire population. This paper describes the theoretical basis for the proposed approach and presents experimental results on ...
Identifying Independencies in Causal Graphs with Feedback
 In Uncertainty in Artificial Intelligence: Proceedings of the Twelfth Conference
, 1996
"... We show that the dseparation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables. 1 INTRODUCTION It is well known that the dseparation test is sound and complete relative to the independencies assumed in t ..."
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Cited by 19 (0 self)
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We show that the dseparation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables. 1 INTRODUCTION It is well known that the dseparation test is sound and complete relative to the independencies assumed in the construction of Bayesian networks [Verma and Pearl, 1988, Geiger et al., 1990]. In other words, any dseparation condition in the network corresponds to a genuine independence condition in the underlying probability distribution and, conversely, every dconnection corresponds to a dependency in at least one distribution compatible with the network. The situation with feedback systems is more complicated, primarily because the probability distributions associated with such systems do not lend themselves to a simple product decomposition. The joint distribution of feedback systems cannot be written as a product of the conditional distributions of each child variable, given its parents. Rath...
Useful Counterfactuals
 ETAI (ELECTRONIC TRANSACTIONS ON ARTIFICIAL INTELLIGENCE
, 1999
"... Counterfactual conditional sentences can be useful in articial intelligence as they are in human aairs. In particular, they allow reasoners to learn from experiences that they did not quite have. Our tools for making inferences from counterfactuals permit inferring sentences that are not themselves ..."
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Cited by 17 (2 self)
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Counterfactual conditional sentences can be useful in articial intelligence as they are in human aairs. In particular, they allow reasoners to learn from experiences that they did not quite have. Our tools for making inferences from counterfactuals permit inferring sentences that are not themselves counterfactual. This is what makes them useful. A simple class of useful counterfactuals involves a change of one component of a point in a space provided with a cartesian product structure. We call these cartesian counterfactuals. Cartesian counterfactuals can be modeled by assignment and contents functions as in program semantics. We also consider the more general treestructured counterfactuals.