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51
Structurebased causes and explanations in the independent choice logic
 Proceedings UAI2003
, 2003
"... This paper is directed towards combining Pearl’s structuralmodel approach to causal reasoning with highlevel formalisms for reasoning about actions. More precisely, we present a combination of Pearl’s structuralmodel approach with Poole’s independent choice logic. We show how probabilistic theor ..."
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Cited by 13 (6 self)
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This paper is directed towards combining Pearl’s structuralmodel approach to causal reasoning with highlevel formalisms for reasoning about actions. More precisely, we present a combination of Pearl’s structuralmodel approach with Poole’s independent choice logic. We show how probabilistic theories in the independent choice logic can be mapped to probabilistic causal models. This mapping provides the independent choice logic with appealing concepts of causality and explanation from the structuralmodel approach. We illustrate this along Halpern and Pearl’s sophisticated notions of actual cause, explanation, and partial explanation. Furthermore, this mapping also adds firstorder modeling capabilities and explicit actions to the structuralmodel approach.
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 of counterfactuals, ..."
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Cited by 11 (5 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.
Settable Systems: An Extension of Pearl’s Causal Model with Optimization, Equilibium, and Learning
, 2008
"... Judea Pearl’s Causal Model is a rich framework that provides deep insight into the nature of causal relations. As yet, however, the Pearl Causal Model (PCM) has not had much impact on economics or econometrics. This may be due in part to the fact that the PCM is not as well suited to analyzing econo ..."
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Cited by 11 (6 self)
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Judea Pearl’s Causal Model is a rich framework that provides deep insight into the nature of causal relations. As yet, however, the Pearl Causal Model (PCM) has not had much impact on economics or econometrics. This may be due in part to the fact that the PCM is not as well suited to analyzing economic structures as might be desired. We o¤er the settable systems framework as an extension of the PCM that embodies features of central interest to economists and econometricians: optimization, equilibrium, and learning. Because these are common features of physical, natural, or social systems, our framework may prove generally useful. In particular, settable systems o¤er a number of advantages relative to the PCM for machine learning. Important distinguishing features of the settable systems framework are its countable dimensionality, its treatment of attributes, the absence of a …xedpoint requirement, and the use of partitioning and partitionspeci…c response functions to accommodate the behavior of optimizing and interacting agents. A series of closely related machine learning examples and examples from game theory and machine learning with feedback demonstrates limitations of the PCM and motivates the distinguishing features of settable systems.
Causes and Explanations in the StructuralModel Approach: Tractable Cases
 IN PROC. EIGHTEENTH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI 2002
, 2002
"... In this paper, we continue our research on the algorithmic aspects of Halpern and Pearl's causes and explanations in the structuralmodel approach. To this end, we present new characterizations of weak causes for certain classes of causal models, which show that under suitable restrictions deciding ..."
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Cited by 10 (3 self)
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In this paper, we continue our research on the algorithmic aspects of Halpern and Pearl's causes and explanations in the structuralmodel approach. To this end, we present new characterizations of weak causes for certain classes of causal models, which show that under suitable restrictions deciding causes and explanations is tractable. To our knowledge, these are the first explicit tractability results for the structuralmodel approach.
Trygve Haavelmo and the Emergence of Causal Calculus
, 2012
"... Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. Th ..."
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Cited by 8 (1 self)
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Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex policy and counterfactual questions using embarrassingly simple routines, some by mere inspection of the model’s structure. Several such problems are illustrated by examples, including misspecification tests, identification, mediation and introspection. Finally, we observe that modern economists are largely unaware of the benefits that Haavelmo’s ideas bestow upon them and, as a result, econometric research has not fully utilized modern advances in causal analysis. 1
Complete Identification Methods for the Causal Hierarchy
"... We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variabl ..."
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Cited by 7 (2 self)
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We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variables; causeeffect relationships, derived from distributions resulting from external interventions; and counterfactuals, derived from distributions that span multiple “parallel worlds ” and resulting from simultaneous, possibly conflicting observations and interventions. We completely characterize cases where a given causal query can be computed from information lower in the hierarchy, and provide algorithms that accomplish this computation. Specifically, we show when effects of interventions can be computed from observational studies, and when probabilities of counterfactuals can be computed from experimental studies. We also provide a graphical characterization of those queries which cannot be computed (by any method) from queries at a lower layer of the hierarchy.
Complexity Results for Explanations in the StructuralModel Approach
 Institut für Informationssysteme
, 2002
"... We analyze the computational complexity of Halpern and Pearl's (causal) explanations in the structuralmodel approach, which are based on their notions of weak and actual causality. In particular, we give a precise picture of the complexity of deciding explanations, partial explanations, and ..."
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Cited by 6 (5 self)
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We analyze the computational complexity of Halpern and Pearl's (causal) explanations in the structuralmodel approach, which are based on their notions of weak and actual causality. In particular, we give a precise picture of the complexity of deciding explanations, partial explanations, and partial explanations, and of computing the explanatory power of partial explanations.
Using Counterfactuals in KnowledgeBased Programming
 Distributed Computing
, 1998
"... : We show how counterfactuals can be added to the framework of knowledgebased programs of Fagin, Halpern, Moses, and Vardi [1995, 1997]. We show that counterfactuals allow us to capture in a natural way notions like minimizing the number of messages that are sent, whereas attempts to formalize these ..."
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Cited by 6 (3 self)
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: We show how counterfactuals can be added to the framework of knowledgebased programs of Fagin, Halpern, Moses, and Vardi [1995, 1997]. We show that counterfactuals allow us to capture in a natural way notions like minimizing the number of messages that are sent, whereas attempts to formalize these notions without counterfactuals lead to some rather counterintuitive behavior. We also show how knowledgebased programs with counterfactuals can capture subgameperfect equilibria in games of perfect information. 1 Introduction Knowledgebased programs, first introduced in [Halpern and Fagin 1989] and further developed by Fagin, Halpern, Moses, and Vardi [1995, 1997], are intended to provide a highlevel framework for the design and specification of protocols. Their key feature is that of allowing explicit tests for knowledge. Thus, a knowledgebased program might have the form if K(x = 0) then y := y + 1 else skip; where K(x = 0) should be read as "you know x = 0" and skip is the actio...
The Foundations of Causal Inference
 SUBMITTED TO SOCIOLOGICAL METHODOLOGY.
, 2010
"... This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of ..."
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Cited by 6 (2 self)
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This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 195060s, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring the effects of potential interventions (also called “causal effects” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and nonlinear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and