A causal relationship is represented with a data structure including target identification information for identifying a target, index identification information of each of indexes to be used for quantitatively describing an event that occurs to the target, and causal relationship information for each pair of two indexes selected from the indexes. The causal relationship information includes direction information representing which one of the two indexes is a cause index and which one is an effect index, strength information representing a causal strength between the cause index and the effect index, time information representing a delay time for propagation of an influence of the cause index to the effect index, and correlation information representing a direction of change in the effect index with respect to an increase or decrease in the cause index.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data structure for a causal relationship, comprising: target identification information for identifying a target for which a causal relationship is represented; index identification information of each of a plurality of indexes used for quantitatively describing an event that occurs to the target; and causal relationship information for each pair of two different indexes selected from the plurality of indexes, the causal relationship information including direction information representing which one of the two indexes is a cause index and which one is an effect index, strength information representing a causal strength between the cause index and the effect index, time information representing a delay time for propagation of an influence of the cause index to the effect index, and correlation information representing a direction of change in the effect index with respect to an increase or decrease in the cause index. 2. The data structure for a causal relationship according to claim 1, wherein the strength information represents a causal strength between a change in the cause index and a change in the effect index after a delay time represented by the time information. 3. The data structure for a causal relationship according to claim 2, wherein the time information represents a delay time with which the causal strength between the cause index and the effect index becomes maximum, and the strength information represents a maximum causal strength between the cause index and the effect index. 4. The data structure for a causal relationship according to claim 2, wherein the time information includes information of n delay times from first to n-th (where n is an integer greater than or equal to 2), and the strength information includes information of n causal strengths corresponding to the respective first to n-th delay times. 5. The data structure for a causal relationship according to claim 1, wherein the correlation information represents a correlation coefficient between a value of the cause index and a value of the effect index after a delay time represented by the time information. 6. A causal network generation system to generate a causal network representing a causal relationship between a plurality of events that occur to a target, the causal network generation system comprising: a data acquisition unit that acquires time series data of each of a plurality of indexes used for quantitatively describing the plurality of events; a causal relationship evaluation unit that evaluates, for each pair of two different indexes selected from the plurality of indexes, a causal relationship between the two indexes; and an output unit that outputs data describing a causal relationship for each pair of the two indexes evaluated by the causal relationship evaluation unit, the causal relationship evaluation unit assuming one of the two indexes as a cause index and the other as an effect index, and calculating a causal strength between a change in the cause index and a change in the effect index after a time s while varying a value of the time s to evaluate a causal strength between the cause index and the effect index and a delay time for propagation of an influence of the cause index to the effect index. 7. The causal network generation system according to claim 6, wherein the causal relationship evaluation unit evaluates a delay time with which the causal strength between the cause index and the effect index becomes maximum, and the causal relationship output from the output unit includes information of the delay time with which the causal strength between the cause index and the effect index becomes maximum and information of a maximum causal strength between the cause index and the effect index. 8. The causal network generation system according to claim 6, wherein the causal relationship evaluation unit evaluates the causal strength between the cause index and the effect index for n delay times from first to n-th (where n is an integer greater than or equal to 2), and the causal relationship output from the output unit includes information of n delay times from first to n-th and information of n causal strengths corresponding to the respective first to n-th delay times. 9. The causal network generation system according to claim 6, wherein the causal relationship evaluation unit calculates a correlation coefficient between a value of the cause index and a value of the effect index after the delay time, and the causal relationship output from the output unit includes information of the correlation coefficient. 10. The causal network generation system according to claim 6, wherein the causal relationship evaluation unit evaluates a causal strength between the cause index and the effect index using transfer entropy.