Belief Evolution Network-based Probability Transformation and Fusion

Smets proposes the Pignistic Probability Transformation (PPT) as the decision layer in the Transferable Belief Model (TBM), which argues when there is no more information, we have to make a decision using a Probability Mass Function (PMF). In this paper, the Belief Evolution Network (BEN) and the full causality function are proposed by introducing causality in Hierarchical Hypothesis Space (HHS). Based on BEN, we interpret the PPT from an information fusion view and propose a new Probability Transformation (PT) method called Full Causality Probability Transformation (FCPT), which has better performance under Bi-Criteria evaluation. Besides, we heuristically propose a new probability fusion method based on FCPT. Compared with Dempster Rule of Combination (DRC), the proposed method has more reasonable result when fusing same evidence

The causal foundations of applied probability and statistics

Statistical science (as opposed to mathematical statistics) involves far more than probability theory, for it requires realistic causal models of data generators - even for purely descriptive goals. Statistical decision theory requires more causality: Rational decisions are actions taken to minimize costs while maximizing benefits, and thus require explication of causes of loss and gain. Competent statistical practice thus integrates logic, context, and probability into scientific inference and decision using narratives filled with causality. This reality was seen and accounted for intuitively by the founders of modern statistics, but was not well recognized in the ensuing statistical theory (which focused instead on the causally inert properties of probability measures). Nonetheless, both statistical foundations and basic statistics can and should be taught using formal causal models. The causal view of statistical science fits within a broader information-processing framework which illuminates and unifies frequentist, Bayesian, and related probability-based foundations of statistics. Causality theory can thus be seen as a key component connecting computation to contextual information, not extra-statistical but instead essential for sound statistical training and applications.Comment: 22 pages; in press for Dechter, R., Halpern, J., and Geffner, H., eds. Probabilistic and Causal Inference: The Works of Judea Pearl. ACM book

A Data Mining Approach to Modeling Customer Preference: A Case Study of Intel Corporation

abstract: Understanding customer preference is crucial for new product planning and marketing decisions. This thesis explores how historical data can be leveraged to understand and predict customer preference. This thesis presents a decision support framework that provides a holistic view on customer preference by following a two-phase procedure. Phase-1 uses cluster analysis to create product profiles based on which customer profiles are derived. Phase-2 then delves deep into each of the customer profiles and investigates causality behind their preference using Bayesian networks. This thesis illustrates the working of the framework using the case of Intel Corporation, world’s largest semiconductor manufacturing company.Dissertation/ThesisMasters Thesis Industrial Engineering 201

Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates

Causality has been recently introduced in databases, to model, characterize and possibly compute causes for query results (answers). Connections between query causality and consistency-based diagnosis and database repairs (wrt. integrity constrain violations) have been established in the literature. In this work we establish connections between query causality and abductive diagnosis and the view-update problem. The unveiled relationships allow us to obtain new complexity results for query causality -the main focus of our work- and also for the two other areas.Comment: To appear in Proc. UAI Causal Inference Workshop, 2015. One example was fixe

An action-related theory of causality

The paper begins with a discussion of Russell?s view that the notion of cause is unnecessary for science, and can therefore be eliminated. It is argued that this is true for theoretical physics, but untrue for medicine where the notion of cause plays a central role. Medical theories are closely connected with practical action (attempts to cure and prevent disease), whereas theoretical physics is more remote from applications. This suggests the view that causal laws are appropriate in a context where there is a close connection to action. This leads to a development of an action-related theory of causality which is similar to the agency theory of Menzies and Price, but differs from it in a number of respects, one of which is the following. Menzies and Price connect ?A causes B? with an action to produce B by instantiating A, but, particularly in the case of medicine, the law can also be linked to the action of trying to avoid B by ensuring that A is not instantiated. The action-related theory has in common with agency theory of Menzies and Price, the ability to explain causal asymmetry in a simple fashion, but the introduction of avoidance actions together with some ideas taken form Russell enable some of the objections to agency accounts of causality to be met. The paper begins with a discussion of Russell?s view that the notion of cause is unnecessary for science, and can therefore be eliminated. It is argued that this is true for theoretical physics, but untrue for medicine where the notion of cause plays a central role. Medical theories are closely connected with practical action (attempts to cure and prevent disease), whereas theoretical physics is more remote from applications. This suggests the view that causal laws are appropriate in a context where there is a close connection to action. This leads to a development of an action-related theory of causality which is similar to the agency theory of Menzies and Price, but differs from it in a number of respects, one of which is the following. Menzies and Price connect ?A causes B? with an action to produce B by instantiating A, but, particularly in the case of medicine, the law can also be linked to the action of trying to avoid B by ensuring that A is not instantiated. The action-related theory has in common with agency theory of Menzies and Price, the ability to explain causal asymmetry in a simple fashion, but the introduction of avoidance actions together with some ideas taken form Russell enable some of the objections to agency accounts of causality to be met

Disentangling extrinsic and intrinsic motivations: the case of French GPs dealing with prevention

The economic literature attaches great importance to the analysis of "professional motivations", in particular examining the possible crowding-out effects between extrinsic and intrinsic motivations. This article applies these questions to the healthcare professions with a view to providing a fair scaling of the implementation of pay-for-performance policies by public decision-makers. We assemble a panel of 528 independent general practitioners in the "Provence-Alpes-Côte d’Azur" region in France and provide an inter-personal statistical decomposition between extrinsic and intrinsic motivations with regard to preventive actions. The proportion of intrinsic motivations is relatively greater among physicians paid with fixed fees. The significant effect of age describes a U shape which can be interpreted as being the result of a "life cycle of medical motivations". Finally, econometric estimations demonstrate a correlation between a small proportion of intrinsic motivation and a feeling of injustice with regard to the reforms. The cross-sectional nature of the data does not allow us to draw any conclusions concerning the direction of the causality. But the above correlation would seem to support the theory that the implementation of a policy based on monetary incentives towards performance is perceived as being offensive and may be accompanied by a reduction in intrinsic motivations in medical practice.General practitioners, Motivations, Prevention, Payment for performance, Intrinsic and extrinsic incentives, France

From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back

In this work we establish and investigate connections between causes for query answers in databases, database repairs wrt. denial constraints, and consistency-based diagnosis. The first two are relatively new research areas in databases, and the third one is an established subject in knowledge representation. We show how to obtain database repairs from causes, and the other way around. Causality problems are formulated as diagnosis problems, and the diagnoses provide causes and their responsibilities. The vast body of research on database repairs can be applied to the newer problems of computing actual causes for query answers and their responsibilities. These connections, which are interesting per se, allow us, after a transition -inspired by consistency-based diagnosis- to computational problems on hitting sets and vertex covers in hypergraphs, to obtain several new algorithmic and complexity results for database causality.Comment: To appear in Theory of Computing Systems. By invitation to special issue with extended papers from ICDT 2015 (paper arXiv:1412.4311