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The Balanced Survivor Average Causal Effect
The International Journal of Biostatistics, 2013: Statistical analysis of longitudinal outcomes is often complicated by the absence of observable values in patients who die prior to their scheduled measurement. In such cases, the longitudinal data are said to be “truncated by death” to emphasize that the longitudinal measurements are not simply missing, but are undefined after death.
Greene, Tom +4 more
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Causal Discovery and Causal Effect Identification
Understanding how changes in an environment affect outcomes is at the core of causal inference. Unlike standard prediction tasks that capture associations between variables, causal inference aims to reveal what happens when we intervene in a system. In this field, there is a common pipeline consisting of two main problems: causal discovery and causal ...openaire +1 more source
Granger-Causality and Policy Effectiveness
Economica, 1984It is generally recognized that, if a set of monetary and fiscal policy variables Granger-cause1 real economic variables, this does not imply that alternative deterministic rules for determining the values of these policy instruments will alter the joint density function of the real variables.2 It has, however, also been asserted that (letting X denote
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2 Interventions, Causal Effects, and Causal Relevance
2007AbstractThis chapter presents and explicates several concepts—namely, those listed in the chapter title—concerning causality and probability that play a fundamental role in the treatment of extrapolation in heterogeneous populations developed in the remainder of the book.
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Two metamodels of causal effects
Scandinavian Journal of Psychology, 1991Two metamodels, termed Model S and Model V, are proposed for definition, measurement, and generalization of quantitative causal effects. The effect is defined as a part change in score in Model S and as a part change in variance in Model V. Two additional changes, total and remainder change, are defined.
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Causality, Effectiveness, Determinism
2013The author describes his conception of the relationship of such philosophical concepts as causality, effectiveness and determinism. The notions of material causality and teleonomic causality are compared. The study shows the difference between the doctrines of monocausalism and conditionalism. Causality is interpreted as a special case of effectiveness,
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Causal Data Science: Estimating Identifiable Causal Effects
Causal inference is central to scientific inquiry and decision-making across numerous disciplines, including the social sciences, economics, biology, and medicine. Drawing causal conclusions from data fundamentally involves two primary tasks: first, causal effect identification, which is determining if a causal effect can be computed from available ...openaire +1 more source
Statistical Inference for causal Effects
2011Research questions motivating many scientific studies are causal in nature. Causal questions arise in medicine (e.g., how effective is a drug treatment?), economics (e.g., what are the effects of job training programs?), sociology (e.g., is there discrimination in labor markets?), customer satisfaction (e.g., what are the effects of different ways of ...
MEALLI, FABRIZIA +2 more
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