Above are papers related to the following paper: [Paper_ID] Paper #523574. 	[Title] [6] D. Geiger. Graphoids: a qualitative framework for probabilistic inference. An introduction to algorithms for [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #714208. 	[Title] A Note on Testing Exogeneity of Instrumental Variables (DRAFT PAPER) [Abstract]  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 #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 #1134346. 	[Title] group, and despite having just 337 subjects, the study strongly supports Identification of causal effects [Abstract] Figure 8a and Figure 8b show the prior distribution over f(-CR ) that follows from the flat prior and the skewed prior, respectively. Figure 8c and Figure 8d show the posterior distribution p(f (-CR jD)) obtained by our system when run on the Lipid data, using the flat prior and the skewed prior, respectively. From the bounds of Balke and Pearl (1994), it follows that under the large-sample assumption, 0:51 f (-CR jD) 0:86. Figure 8: Prior (a, b) and posterior (c,d) distributions for a subpopulation f (-CR jD) specified by the counter-factual query "Would Joe have improved had he taken the drug, given that he did not improve without it". (a) corresponds to the flat prior, (b) to the skewed prior. This paper identifies and demonstrates a new application area for network-based inference techniques - the management of causal analysis in clinical experimentation. These techniques, which were originally developed for medical diagnosis, are shown capable of circumventing one of the major problems in clinical experiments the assessment of treatment efficacy in the face of imperfect compliance. While standard diagnosis involves purely probabilistic inference in fully specified networks, causal analysis involves partially specified networks in which the links are given causal interpretation and where the domain of some variables are unknown. The system presented in this paper provides the clinical research community, we believe for the first time, an assumption-free, unbiased assessment of the average treatment effect. We offer this system as a practical tool to be used whenever full compliance cannot be enforced and, more broadly, whenever the data available is insufficient for answering the queries of interest to the clinical investigator. Lipid Research Clinic Program. 1984. The lipid research clinics coronary primary prevention trial results, parts i and ii. Journal of the American Medical Association 251(3):351-374. January. 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 #189774. 	[Title] Causal Inference from Indirect Experiments [Abstract] Indirect experiments are studies in which randomized control is replaced by randomized encouragement, that is, subjects are encouraged, rather than forced to receive treatment programs. The purpose of this paper is to bring to the attention of experimental researchers simple mathematical results that enable us to assess, from indirect experiments, the strength with which causal influences operate among variables of interest. The results reveal that despite the laxity of the encouraging instrument, indirect experimentation can yield significant and sometimes accurate information on the impact of a program on the population as a whole, as well as on the particular individuals who participated in the program. 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 #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 #15987. 	[Title] "Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian [Abstract] 8] Dori, D. and Tarsi, M., "A Simple Algorithm to Construct a Consistent Extension of a Partially Oriented Graph," Computer Science Department, Tel-Aviv University. Also Technical Report R-185, UCLA, Cognitive Systems Laboratory, October 1992. [14] Pearl, J. and Wermuth, N., "When Can Association Graphs Admit a Causal Interpretation?," UCLA, Cognitive Systems Laboratory, Technical Report R-183-L, November 1992. [17] Verma, T.S. and Pearl, J., "Deciding Morality of Graphs is NP-complete," Technical Report R-188, UCLA, Cognitive Systems Laboratory, October 1992. 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.

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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 #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 #714975. 	[Title] Using Path Diagrams as a Structural Equation Modelling Tool [Abstract]  Please summarise all the given information.

Paper #523574 has the title `[6] D. Geiger. Graphoids: a qualitative framework for probabilistic inference. An introduction to algorithms for
` and the abstract `nan`.
Question: Which category should Paper #523574 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #523574 has the title `[6] D. Geiger. Graphoids: a qualitative framework for probabilistic inference. An introduction to algorithms for
` and the abstract `nan`.
Question: Which category should Paper #523574 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