Above are papers related to the following paper: [Paper_ID] Paper #714256. 	[Title] A Probabilistic Calculus of Actions [Abstract] 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. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #653441. 	[Title] FROM BAYESIAN NETWORKS TO CAUSAL NETWORKS [Abstract] This paper demonstrates the use of graphs as a mathematical tool for expressing independencies, and as a formal language for communicating and processing causal information for decision 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 theory of Bayesian networks 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 non-experimental data, the effects of external interventions and to specify conditions under which randomized experiments are not necessary. As an example, we show how the effect of smoking on lung cancer can be quantified from non-experimental data, using a minimal set of qualitative assumptions. Finally, the paper offers a graphical interpretation for Rubin's model of causal effects, and demonstrates its equivalence to the manipulative account of causation. We exemplify the tradeoffs between the two approaches by deriving nonparametric bounds on treatment effects under conditions of imperfect compliance. fl Portions of this paper were presented at the 49th Session of the International Statistical Institute, Florence, Italy, August 25 - September 3, 1993. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #523394. 	[Title] Automated Discovery of Linear Feedback Models 1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #189571. 	[Title] Probabilistic evaluation of counterfactual queries [Abstract] To appear in the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, WA, July 31 August 4, 1994. Technical Report R-213-A April, 1994 Abstract Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is interpreted as an external action that forces the proposition A to be true, which is consistent with Lewis' Miraculous Analysis. This formalism offers a concrete embodiment of the "closest world" approach which (1) properly reflects common understanding of causal influences, (2) deals with the uncertainties inherent in the world, and (3) is amenable to machine representation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #189572. 	[Title] Counterfactuals and Policy Analysis in Structural Models [Abstract] Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, determination of liability, and policy analysis. We present a method for evaluating counter-factuals when the underlying causal model is represented by structural models a nonlinear generalization of the simultaneous equations models commonly used in econometrics and social sciences. This new method provides a coherent means for evaluating policies involving the control of variables which, prior to enacting the policy were influenced by other variables in the system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #16008. 	[Title] [12] J. Whittaker. Graphical Models in Applied Mathematical Multivariate Statis- [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1134348. 	[Title] On The Foundation Of Structural Equation Models or [Abstract] When Can We Give Causal Interpretation Abstract The assumptions underlying statistical estimation are of fundamentally different character from the causal assumptions that underly structural equation models (SEM). The differences have been blurred through the years for the lack of a mathematical notation capable of distinguishing causal from equational relationships. Recent advances in graphical methods provide formal explication of these differences, and are destined to have profound impact on SEM's practice and philosophy. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #294030. 	[Title] MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains [Abstract] Given a problem, a case-based reasoning (CBR) system will search its case memory and use the stored cases to find the solution, possibly modifying retrieved cases to adapt to the required input specifications. In discrete domains CBR reasoning can be based on a rigorous Bayesian probability propagation algorithm. Such a Bayesian CBR system can be implemented as a probabilistic feedforward neural network with one of the layers representing the cases. In this paper we introduce a Minimum Description Length (MDL) based learning algorithm to obtain the proper network structure with the associated conditional probabilities. This algorithm together with the resulting neural network implementation provide a massively parallel architecture for solving the efficiency bottleneck in case-based reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #714260. 	[Title] Probabilistic evaluation of sequential plans from causal models with hidden variables [Abstract] The paper concerns the probabilistic evaluation of plans in the presence of unmeasured variables, each plan consisting of several concurrent or sequential actions. We establish a graphical criterion for recognizing when the effects of a given plan can be predicted from passive observations on measured variables only. When the criterion is satisfied, a closed-form expression is provided for the probability that the plan will achieve a specified goal. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #149669. 	[Title] Reasoning about Time and Probability [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521855. 	[Title] Decision-Theoretic Foundations for Causal Reasoning [Abstract] We present a definition of cause and effect in terms of decision-theoretic 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. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #714289. 	[Title] ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY [Abstract] 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 non-experimental data, the effects of external interventions and to specify conditions under which randomized experiments are not necessary. Finally, the paper offers a graphical interpretation for Rubin's model of causal effects, and demonstrates its equivalence to the manipulative account of causation. We exemplify the tradeoffs between the two approaches by deriving nonparametric bounds on treatment effects under conditions of imperfect compliance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #65074. 	[Title] Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. [Abstract] We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154229. 	[Title] Belief Networks Revisited [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154232. 	[Title]  [Abstract] In the Proceedings of the Conference on Uncertainty in Artificial Intelli- gence (UAI-94), Seattle, WA, 46-54, July 29-31, 1994. Technical Report R-213-B April, 1994 Abstract Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [Balke and Pearl, 1994], where the antecedent of the query is interpreted as an external action that forces the proposition A to be true. When a prior probability is available on the causal mechanisms governing the domain, counterfactual probabilities can be evaluated precisely. However, when causal knowledge is specified as conditional probabilities on the observables, only bounds can computed. This paper develops techniques for evaluating these bounds, and demonstrates their use in two applications: (1) the determination of treatment efficacy from studies in which subjects may choose their own treatment, and (2) the determination of liability in product-safety litigation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154233. 	[Title] Bayesian Networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #559804. 	[Title] On the Testability of Causal Models with Latent and Instrumental Variables [Abstract] Certain causal models involving unmeasured variables induce no independence constraints among the observed variables but imply, nevertheless, inequality constraints on the observed distribution. This paper derives a general formula for such inequality constraints as induced by instrumental variables, that is, exogenous variables that directly affect some variables but not all. With the help of this formula, it is possible to test whether a model involving instrumental variables may account for the data, or, conversely, whether a given vari able can be deemed instrumental. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #73162. 	[Title] Causal diagrams for empirical research [Abstract] The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in terms of observed distributions; otherwise, the diagrams can be queried to suggest additional observations or auxiliary experiments from which the desired inferences can be obtained. Key words: Causal inference, graph models, structural equations, treatment effect. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39890. 	[Title] A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task [Abstract] This paper concerns the empirical basis of causation, and addresses the following issues: We propose a minimal-model semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. We provide an effective algorithm for inferred causation and show that, for a large class of data the algorithm can uncover the direction of causal influences as defined above. Finally, we ad dress the issue of non-temporal causation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #189655. 	[Title] Axioms of Causal Relevance [Abstract] This paper develops axioms and formal semantics for statements of the form "X is causally irrelevant to Y in context Z," which we interpret to mean "Changing X will not affect Y if we hold Z constant." The axiomization of causal irrelevance is contrasted with the axiomization of informational irrelevance, as in "Learning X will not alter our belief in Y , once we know Z." Two versions of causal irrelevance are analyzed, probabilistic and deterministic. We show that, unless stability is assumed, the probabilistic definition yields a very loose structure, that is governed by just two trivial axioms. Under the stability assumption, probabilistic causal irrelevance is isomorphic to path interception in cyclic graphs. Under the deterministic definition, causal irrelevance complies with all of the axioms of path interception in cyclic graphs, with the exception of transitivity. We compare our formalism to that of [Lewis, 1973], and offer a graphical method of proving theorems about causal relevance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #714975. 	[Title] Using Path Diagrams as a Structural Equation Modelling Tool [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51045. 	[Title] Regression Can Build Predictive Causal Models [Abstract] Covariance information can help an algorithm search for predictive causal models and estimate the strengths of causal relationships. This information should not be discarded after conditional independence constraints are identified, as is usual in contemporary causal induction algorithms. Our fbd algorithm combines covariance information with an effective heuristic to build predictive causal models. We demonstrate that fbd is accurate and efficient. In one experiment we assess fbd's ability to find the best predictors for variables; in another we compare its performance, using many measures, with Pearl and Verma's ic algorithm. And although fbd is based on multiple linear regression, we cite evidence that it performs well on problems that are very difficult for regression algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #242663. 	[Title] Two Algorithms for Inducing Structural Equation Models from Data [Abstract] We present two algorithms for inducing structural equation models from data. Assuming no latent variables, these models have a causal interpretation and their parameters may be estimated by linear multiple regression. Our algorithms are comparable with PC [15] and IC [12, 11], which rely on conditional independence. We present the algorithms and empirical comparisons with PC and IC. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #162664. 	[Title] An Algorithm for the Construction of Bayesian Network Structures from Data [Abstract] Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the database which is then used to recover the underlying Bayesian network structure using a non CI based method. Results of preliminary evaluation of the algorithm on two networks (ALARM and LED) are presented. We also discuss some algo rithm performance issues and open problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123689. 	[Title] A Parallel Learning Algorithm for Bayesian Inference Networks [Abstract] We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning algorithm exploits both properties of the MDL-based score metric, and a distributed, asynchronous, adaptive search technique called nagging. Nagging is intrinsically fault tolerant, has dynamic load balancing features, and scales well. We demonstrate the viability, effectiveness, and scalability of our approach empirically with several experiments using on the order of 20 machines. More specifically, we show that our distributed algorithm can provide optimal solutions for larger problems as well as good solutions for Bayesian networks of up to 150 variables. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51049. 	[Title] Causal inference, path analysis, and recursive struc-tural equations models. In C. Clogg, editor, Sociological Methodology, [Abstract] Lipid Research Clinic Program 84] Lipid Research Clinic Program. The Lipid Research Clinics Coronary Primary Prevention Trial results, parts I and II. Journal of the American Medical Association, 251(3):351-374, January 1984. [Pearl 93] Judea Pearl. Aspects of graphical models connected with causality. Technical Report R-195-LL, Cognitive Systems Laboratory, UCLA, June 1993. Submitted to Biometrika (June 1993). Short version in Proceedings of the 49th Session of the International Statistical Institute: Invited papers, Flo rence, Italy, August 1993, Tome LV, Book 1, pp. 391-401. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3191. 	[Title] Model Selection and Accounting for Model Uncertainty in Linear Regression Models [Abstract] 1 Adrian E. Raftery is Professor of Statistics and Sociology, David Madigan is Assistant Professor of Statistics, and Jennifer Hoeting is a Ph.D. Candidate, all at the Department of Statistics, GN-22, University of Washington, Seattle, WA 98195. The research of Raftery and Hoeting was supported by ONR Contract N-00014-91-J-1074. Madigan's research was partially supported by NSF grant no. DMS 92111627. The authors are grateful to Danika Lew for research assistance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #714748. 	[Title] CAUSATION, ACTION, AND COUNTERFACTUALS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #714879. 	[Title] The New Challenge: From a Century of Statistics to an Age of Causation [Abstract] Some of the main users of statistical methods - economists, social scientists, and epidemiologists are discovering that their fields rest not on statistical but on causal foundations. The blurring of these foundations over the years follows from the lack of mathematical notation capable of distinguishing causal from equational relationships. By providing formal and natural explication of such relations, graphical methods have the potential to revolutionize how statistics is used in knowledge-rich applications. Statisticians, in response, are beginning to realize that causality is not a metaphysical dead-end but a meaningful concept with clear mathematical underpinning. The paper surveys these developments and outlines future challenges. Please summarise all the given information.

Paper #714256 has the title `A Probabilistic Calculus of Actions  
` and the abstract `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.
`.
Question: Which category should Paper #714256 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #714256 has the title `A Probabilistic Calculus of Actions  
` and the abstract `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.
`.
Question: Which category should Paper #714256 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods