Exploring Causal Influences

Recent data mining techniques exploit patterns of statistical independence in multivariate data to make conjectures about cause/effect relationships. These relationships can be used to construct causal graphs, which are sometimes represented by weighted node-link diagrams, with nodes representing variables and combinations of weighted links and/or nodes showing the strength of causal relationships. We present an interactive visualization for causal graphs (ICGs), inspired in part by the Influence Explorer. The key principles of this visualization are as follows: Variables are represented with vertical bars attached to nodes in a graph. Direct manipulation of variables is achieved by sliding a variable value up and down, which reveals causality by producing instantaneous change in causally and/or probabilistically linked variables. This direct manipulation technique gives users the impression they are causally influencing the variables linked to the one they are manipulating. In this context, we demonstrate the subtle distinction between seeing and setting of variable values, and in an extended example, show how this visualization can help a user understand the relationships in a large variable set, and with some intuitions about the domain and a few basic concepts, quickly detect bugs in causal models constructed from these data mining techniques

What Might a Theory of Causation Do for Sport?

The purpose of this research is to articulate how a theory of causation might be serviceable to a theory of sport. This article makes conceptual links between Bernard Suits’ theory of game-playing, causation, and theories of causation. It justifies theories of causation while drawing on connections between sport and counterfactuals. It articulates the value of theories of causation while emphasizing possible limitations. A singularist theory of causation is found to be more broadly serviceable with particular regard to its analysis of sports

Risk Objectivism and Risk Subjectivism: When Are Risks Real

Typically, those who discuss Risk management envision a two-step process wherein, first, Risk is more or less objectively appraised and, second, the acceptability of those Risks is subjectively evaluated. This paper questions the philosophical foundations of that approach

On Probabilistic Causalities between Activities, Obvious Social Stimuli, Inferences, and\ud Behavioral Outcomes

The objective of the research was to answer the question: What kinds of causal relationships do\ud persons construct to figure out another person based on minimal information?\ud A theoretical, and a corresponding empirical research were done. The theoretical research\ud produced a hypothesis: Persons, who have an analytic approach to obvious social stimuli, infer\ud more correctly activities of other persons than persons, who have a holistic approach. The\ud hypothesis corroborated. Factor Analysis was applicable to the influence of the researcher and,\ud Householder method, Bayes matrices to the probabilistic causalities. Time reliability was α-\ud reliability, and the coefficients of nondetermination laid foundation to the validity of the\ud observation. The theoretic results indicated. If the persons are able to use the whole outer set of\ud the stimuli available, and case study like deduction, and induction they have the resolution level\ud of the inference that enables them to figure out other persons, more probably. Quite the reverse,\ud if the persons apply to the outer set of stimuli available, partially, employ false generalizations,\ud and agree deeds with persons without reasoning, they have the resolution level that disables them\ud to figure out others persons, more probably

The Cognitive Status of Risk: A Response to Thompson

Discussing the role that probability theory should play in Risk analysis and management, Dr. Valverde argues that Thompson\u27s approach puts too much emphasis on the distinction between Risk subjectivism and Risk objectivism in addressing the question, When are Risks real