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In software testing, a cause–effect graph is a directed graph that maps a set of causes to a set of effects. The causes may be thought of as the input to the program, and the effects may be thought of as the output. Usually the graph shows the nodes representing the causes on the left side and the nodes representing the effects on the right side.
There exist several forms of causal diagrams including Ishikawa diagrams, directed acyclic graphs, causal loop diagrams, [10] and why-because graphs (WBGs). The image below shows a partial why-because graph used to analyze the capsizing of the Herald of Free Enterprise. Partial Why–because graph of the capsizing of the Herald of Free Enterprise
Figure 1 is a causal graph that represents this model specification. Each variable in the model has a corresponding node or vertex in the graph. Additionally, for each equation, arrows are drawn from the independent variables to the dependent variables. These arrows reflect the direction of causation.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. [1] Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the ...
The use of words and arrows (known in network theory as nodes and edges) to construct directed graph models of cause and effect dates back, at least, to the use of path analysis by Sewall Wright in 1918.
Sample Ishikawa diagram shows the causes contributing to problem. The defect, or the problem to be solved, [1] is shown as the fish's head, facing to the right, with the causes extending to the left as fishbones; the ribs branch off the backbone for major causes, with sub-branches for root-causes, to as many levels as required.
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One of the thinking processes in the theory of constraints, a current reality tree (CRT) is a tool to analyze many systems or organizational problems at once. By identifying root causes common to most or all of the problems, a CRT can greatly aid focused improvement of the system.