A Formal Treatment of Sequential Ignorability

Taking a rigorous formal approach, we consider sequential decision problems involving observable variables, unobservable variables, and action variables. We can typically assume the property of extended stability, which allows identification (by means of G-computation) of the consequence of a specified treatment strategy if the unobserved variables are, in fact, observed - but not generally otherwise. However, under certain additional special conditions we can infer simple stability (or sequential ignorability), which supports G-computation based on the observed variables alone. One such additional condition is sequential randomization, where the unobserved variables essentially behave as random noise in their effects on the actions. Another is sequential irrelevance, where the unobserved variables do not influence future observed variables. In the latter case, to deduce sequential ignorability in full generality requires additional positivity conditions. We show here that these positivity conditions are not required when all variables are discrete.Comment: 25 pages, 5 figures, 1 tabl

Extended conditional independence and applications in causal inference

The goal of this paper is to integrate the notions of stochastic conditional independence and variation conditional independence under a more general notion of extended conditional independence. we show that under appropriate assumptions the calculus that applies for the two cases separately (axioms of a separoid) still applies for the extended case. these results provide a rigorous basis for a wide range of statistical concepts, including ancillarity and sufficiency, and, in particular, the decision theoretic framework for statistical causality, which uses the language and calculus of conditional independence in order to express causal properties and make causal inferences

Intermittent DCF: a MAC protocol for Cognitive Radios in Overlay Access Networks

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Performance Evaluation of an Enhanced Uplink 3.5G System for Mobile Healthcare Applications

The present paper studies the prospective and the performance of a forthcoming high-speed third generation (3.5G) networking technology, called enhanced uplink, for delivering mobile health (m-health) applications. The performance of 3.5G networks is a critical factor for successful development of m-health services perceived by end users. In this paper, we propose a methodology for performance assessment based on the joint uplink transmission of voice, real-time video, biological data (such as electrocardiogram, vital signals, and heart sounds), and healthcare records file transfer. Various scenarios were concerned in terms of real-time, nonreal-time, and emergency applications in random locations, where no other system but 3.5G is available. The accomplishment of quality of service (QoS) was explored through a step-by-step improvement of enhanced uplink system's parameters, attributing the network system for the best performance in the context of the desired m-health services

Dark nudges in gambling

‘Nudge’ has come into common usage in behavioral science, the intersection of psychology and economics, for situations where a ‘choice architect’ aligns a system with consumers’ best long - term interests (Thaler & Sunstein, 2008). A cafeteria designer might ‘nudge’ her customers by placing the salad bar centrally, while relegating unhealthier foods to a corner. In this editorial I argue that, in gambling, nudging works differently. Gambling’s ‘dark nudges’ are designed to exploit gamblers’ biases, as economic rationality on the part of gambling firms predicts. Gambling’s dark nudges reveal the contradictions of industry - led responsible gambling initiatives, and show how stronger regulation is required to reverse gambling’s spiralling public health costs (Korn & Shaffer, 1999; Livingstone & Adams, 2011; Markham & Young, 2015; Orford, 2005; Orford, 2010