Generating causal association rankings using dynamic embeddings

A computer system, the computer system comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:
• automatically receiving a window of candidate events including events of a first type preceding one or more target events of interest of a second type, wherein the target events of interest of the second type comprise outcomes or incidents;
• automatically generating contrastive windows of candidate events of the first type preceding the one or more target events of interest, each of the generated contrastive windows of candidate events of the first type corresponding to a different dropped candidate event of the first type from the received window of candidate events;
• automatically generating resulting embeddings for each of the generated contrastive windows of candidate events;
• automatically identifying matching historical windows of candidate events, the matching historical windows of candidate events including events of the first type preceding the one or more target events of interest of the second type, and having resulting embeddings that are below a predetermined threshold distance away from the resulting embeddings generated for a given generated contrastive window of candidate events;
• in response to identifying the matching historical windows of candidate events, automatically calculating and storing a corresponding first score for each of the matching historical window of candidate events;
• automatically identifying matching incident windows corresponding to each identified matching historical window, the matching incident windows having features or textual data corresponding to events of the second type that are above a predetermined similarity value threshold when compared to features or textual data corresponding to the one or more target events of interest, and subsequently calculating corresponding second scores for each of the identified matching incident windows;
• and
• automatically calculating combined causal association scores using the first scores and the second scores, and using the combined causal association scores to generate causal association rankings for the events of the first type in the received window of candidate events.

A computer program product, the computer program product comprising:

• one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising:
• automatically receiving a window of candidate events including events of a first type preceding one or more target events of interest of a second type, wherein the target events of interest of the second type comprise outcomes or incidents;
• automatically generating contrastive windows of candidate events of the first type preceding the one or more target events of interest, each of the generated contrastive windows of candidate events of the first type corresponding to a different dropped candidate event of the first type from the received window of candidate events;
• automatically generating resulting embeddings for each of the generated contrastive windows of candidate events;
• automatically identifying matching historical windows of candidate events, the matching historical windows of candidate events including events of the first type preceding the one or more target events of interest of the second type, and having resulting embeddings that are below a predetermined threshold distance away from the resulting embeddings generated for a given generated contrastive window of candidate events;
• in response to identifying the matching historical windows of candidate events, automatically calculating and storing a corresponding first score for each of the matching historical window of candidate events;
• automatically identifying matching incident windows corresponding to each identified matching historical window, the matching incident windows having features or textual data corresponding to events of the second type that are above a predetermined similarity value threshold when compared to features or textual data corresponding to the one or more target events of interest, and subsequently calculating corresponding second scores for each of the identified matching incident windows;
• and
• automatically calculating combined causal association scores using the first scores and the second scores, and using the combined causal association scores to generate causal association rankings for the events of the first type in the received window of candidate events.