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Graphical Models, Causality, And Intervention
, 1993
"... tion of belief networks is given in [4]. 2 In [3], the graphs were called "causal networks," for which the authors were criticised; they have agreed to refrain from using the word "causal." In the current paper, Spiegelhalter etal. deemphasize the causal interpretation of the a ..."
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Cited by 102 (34 self)
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tion of belief networks is given in [4]. 2 In [3], the graphs were called "causal networks," for which the authors were criticised; they have agreed to refrain from using the word "causal." In the current paper, Spiegelhalter etal. deemphasize the causal interpretation of the arcs in favor of the "irrelevance" interpretation (page 4). I think this retreat is regrettable for two reasons: first, causal associations are the primary source of judgments about irrelevance and, second, rejecting the causal interpretation of arcs prevents us from using graphical models for making legitimate predictions about the effect of actions. Such predictions are indispensable in applications such as treatment management and patient monitoring. the causal model also tells us how these probabilities would change as a result of external interventions in the system. For this reason, causal models (or "structural models" as they are often called) have been the target of relent
DecisionTheoretic Foundations for Causal Reasoning
 Journal of Artificial Intelligence Research
, 1995
"... We present a definition of cause and effect in terms of decisiontheoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that ..."
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Cited by 54 (10 self)
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We present a definition of cause and effect in terms of decisiontheoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning. 1. Introduction Knowledge of cause and effect is crucial for modeling the affects of actions. For example, if we observe a statistical correlation between smoking and lung cancer, we can not conclude from this observation alone that our chances of getting lung cancer will change if we stop smoking. If, however, we als...
A Probabilistic Calculus of Actions
, 1994
"... We present a symbolic machinery that admits both probabilistic and causal information about a given domain, and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P (yj ..."
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Cited by 31 (13 self)
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We present a symbolic machinery that admits both probabilistic and causal information about a given domain, and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P (yjX = x), which represents the observation X = x, and causal conditioning, P (yjdo(X = x)), read: the probability of Y = y conditioned on holding X constant (at x) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions and observations. 1 Introduction Probabilistic methods, especially those based on graphical models have proven useful in tasks of predictions, abduction and belief revision [Pearl 1988, Heckerman 1990, Goldszmidt 1992, Darwiche 1993]. Their use in planning, however, remains less po...
Aspects Of Graphical Models Connected With Causality
, 1993
"... This paper demonstrates the use of graphs as a mathematical tool for expressing independenices, and as a formal language for communicating and processing causal information in statistical analysis. We show how complex information about external interventions can be organized and represented graphica ..."
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Cited by 14 (10 self)
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This paper demonstrates the use of graphs as a mathematical tool for expressing independenices, and as a formal language for communicating and processing causal information in statistical analysis. We show how complex information about external interventions can be organized and represented graphically and, conversely, how the graphical representation can be used to facilitate quantitative predictions of the effects of interventions. We first review the Markovian account of causation and show that directed acyclic graphs (DAGs) offer an economical scheme for representing conditional independence assumptions and for deducing and displaying all the logical consequences of such assumptions. We then introduce the manipulative account of causation and show that any DAG defines a simple transformation which tells us how the probability distribution will change as a result of external interventions in the system. Using this transformation it is possible to quantify, from nonexperimental data...
E01] The Ensemble project, 2001. Available electronically at http://www.cs.cornell.edu/Info/Projects/Ensemble
, 2002
"... novel representation: ..."
On The Identification Of Nonparametric Structural Models
, 1997
"... In this paper we study the identifiability of nonparametric models, that is, models in which both the functional forms of the equations and the probability distributions of the disturbances remain unspecified. Identifiability in such models does not mean uniqueness of parameters but rather uniquenes ..."
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Cited by 2 (1 self)
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In this paper we study the identifiability of nonparametric models, that is, models in which both the functional forms of the equations and the probability distributions of the disturbances remain unspecified. Identifiability in such models does not mean uniqueness of parameters but rather uniqueness of the set of predictions of interest to the investigator. For example, predicting the effects of changes, interventions, and control. We provide sufficient and necessary conditions for identifying a set of causal predictions of the type: "Find the distribution of Y , assuming that X is controlled by external intervention", where Y and X are arbitrary variables of interest. Whenever identifiable, such predictions can be expressed in closed algebraic form, in terms of observed distributions. We also show how the identifying criteria can be verified qualitatively, by inspection, using the graphical representation of the structural model. When compared to standard identifiability tests of lin...
(Draft Copy) On the Statistical Interpretation of Structural Equations
"... an economist might arrive at a model like this: ..."
Session F3F Panel IllStructured Problem Solving in Engineering Education
"... Abstract There is a gap between the problems our students typically encounter in their education and the problems they are likely to be asked to solve in their future employments. It is convenient in education, both in specification and assessment, to provide fairly wellstructured problems, and man ..."
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Abstract There is a gap between the problems our students typically encounter in their education and the problems they are likely to be asked to solve in their future employments. It is convenient in education, both in specification and assessment, to provide fairly wellstructured problems, and many instructors view using such problems as a way to manage the learning process. However, realworld problems are typically illstructured and we argue that using only wellstructured problems as learning examples does not prepare our students for the problems they will encounter in their professional life. Preparing students for dealing with illstructured, or open ended, problems is an educational challenge involving critical thinking skills, which most instructors and curriculum designers view as an important goal of the learning process. This panel is designed to address issues of open or illstructured problems from learning aspects. The panel will also cover concrete examples to inspire education designers preparing students for their future careers by improving their problem solving capabilities through use of illstructured problems as learning examples.
COVARIANCE/INVARIANCE: A COGNITIVE HEURISTIC IN EINSTEIN’S RELATIVITY THEORY FORMATION
"... Relativity Theory by Albert Einstein has been so far little considered by cognitive scientists, notwithstanding its undisputed scientific and philosophical moment. Unfortunately, we don’t have a diary or notebook i as cognitively useful as Faraday’s. But physics historians and philosophers have done ..."
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Relativity Theory by Albert Einstein has been so far little considered by cognitive scientists, notwithstanding its undisputed scientific and philosophical moment. Unfortunately, we don’t have a diary or notebook i as cognitively useful as Faraday’s. But physics historians and philosophers have done a great job that is relevant both for the study of the scientist’s reasoning and the philosophy of science. I will try here to highlight the fertility of a ‘triangulation ’ using cognitive psychology, history of science and philosophy of science in starting answering a clearly very complex question: why did Einstein discover Relativity Theory? Here we are not much concerned with the unending question of precisely what Einstein discovered, that still remains unanswered, for we have no consensus over the exact nature of the theory’s foundations (Norton 1993). We are mainly interested in starting answering the ‘how question’, and especially the following subquestion: what (presumably) were his goals and strategies in his search? I will base my argument on fundamental Einstein’s publications, aiming at pointing out a theoryspecific heuristic, setting both a goal and a strategy: covariance/invariance. The result has significance in theory formation in science, especially in concept and model building. It also raises