In techniques for causal modeling and attribution, a causal modeling application implements a dynamical causal modeling framework. Input data is received as a representation of communications between users, such as social media interactions between social media users, and causal relationships between the users can be determined based in part on the input data that represents the communications. Influence variables, such as exogenous variables and/or endogenous variables, can also be determined that influence the causal relationships between the users. A causal relationships model is generated based on the influence variables and the causal relationships between the users, where the causal relationships model is representative of causality, influence, and attribution between the users.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: receiving input data as a representation of communications between two or more users; determining causal relationships between the two or more users based in part on the input data that represents the communications; determining one or more influence variables that influence the causal relationships between the two or more users; and generating a causal relationships model based on the one or more influence variables and the causal relationships between the two or more users, the causal relationships model representative of causality, influence, and attribution between the two or more users. 2. The method as recited in claim 1, wherein the influence variables include one or more endogenous variables that are moderated by feedback from one or more of the users, the method further comprising: determining the causal relationships between the two or more users based on the one or more endogenous variables that influence the causal relationships. 3. The method as recited in claim 1, wherein the influence variables include one or more exogenous variables, the method further comprising: determining the causal relationships between the two or more users based on the one or more exogenous variables that independently influence the causal relationships without being affected by feedback from the users. 4. The method as recited in claim 1, wherein: the influence variables include one or more endogenous variables that are moderated by feedback from one or more of the users; the influence variables further include one or more exogenous variables that independently influence the causal relationships without being affected by feedback from the users; and the method further comprising: determining the causal relationships between the two or more users based on the input data and simultaneous modeling of the one or more endogenous variables and the one or more exogenous variables. 5. The method as recited in claim 1, further comprising: quantifying the causal relationships with influence scores that each indicate a degree to which one of the users influence a causal relationship with another of the users. 6. The method as recited in claim 1, wherein the causal relationships between the two or more users are non-symmetric, and the causal relationships model is further generated to represent the non-symmetric causal relationships. 7. The method as recited in claim 1, wherein the communications are social media interactions between the two or more users, the method further comprising: applying a dynamical causal modeling framework that implements said determining the causal relationships of the social media interactions. 8. The method as recited in claim 7, wherein the dynamical causal modeling framework accounts for hysteresis corrections associated with one or more of the users. 9. The method as recited in claim 1, further comprising: receiving the communications between the two or more users modeled as text data that includes a sentiment expressed by a user about a subject in the communications; analyzing the text data to identify the sentiment about the subject; and generating the input data as the representation of the communications between the two or more users, the input data comprising vector space representations of the communications that are represented as values. 10. The method as recited in claim 9, wherein the text data is said received as part-of-speech information that includes one or more of noun expressions, verb expressions, and tagged parts-of-speech of the communications. 11. A computing device, comprising: a memory configured to maintain input data that is received as a representation of social media interactions between two or more users; a processor system to implement a causal modeling application that applies a dynamical causal modeling framework that is configured to: determine causal relationships between the two or more users based in part on the input data that represents the social media interactions; determine one or more influence variables that influence the causal relationships between the two or more users; and generate a causal relationships model based on the one or more influence variables and the causal relationships between the two or more users. 12. The computing device as recited in claim 11, wherein: the influence variables include one or more endogenous variables that are moderated by feedback from one or more of the users; and the dynamical causal modeling framework is configured to determine the causal relationships between the two or more users based on the one or more endogenous variables that influence the causal relationships. 13. The computing device as recited in claim 11, wherein: wherein the influence variables include one or more exogenous variables; and the dynamical causal modeling framework is configured to determine the causal relationships between the two or more users based on the one or more exogenous variables that independently influence the causal relationships without being affected by feedback from the users. 14. The computing device as recited in claim 11, wherein: the influence variables include one or more endogenous variables that are moderated by feedback from one or more of the users; the influence variables further include one or more exogenous variables that independently influence the causal relationships without being affected by feedback from the users; and the dynamical causal modeling framework is configured to determine the causal relationships between the two or more users based on the input data and simultaneous modeling of the one or more endogenous variables and the one or more exogenous variables. 15. The computing device as recited in claim 11, wherein the dynamical causal modeling framework is configured to quantify the causal relationships with influence scores that each indicate a degree to which one of the users influence a causal relationship with another of the users. 16. The computing device as recited in claim 11, wherein the causal relationships between the two or more users are non-symmetric, and the causal relationships model is representative of the non-symmetric causal relationships, influence, and attribution between the two or more users. 17. The computing device as recited in claim 11, wherein the input data that represents the social media interactions between the two or more users comprises vector space representations of the social media interactions that are represented as values. 18. A computer-readable storage memory comprising a causal modeling application stored as instructions that are executable and, responsive to execution of the instructions by a computing device, the computing device performs operations of the causal modeling application comprising to: receive input data as a representation of communications between two or more users; determine causal relationships between the two or more users based in part on the input data that represents the communications; determine one or more influence variables that influence the causal relationships between the two or more users; and generate a causal relationships model based on the one or more influence variables and the causal relationships between the two or more users. 19. The computer-readable storage memory as recited in claim 18, wherein: the influence variables include one or more endogenous variables that are moderated by feedback from one or more of the users; and the computing device performs operations of the causal modeling application further comprising to determine the causal relationships between the two or more users based on the one or more endogenous variables that influence the causal relationships. 20. The computer-readable storage memory as recited in claim 19, wherein: the influence variables include one or more exogenous variables; and the computing device performs operations of the causal modeling application further comprising to determine the causal relationships between the two or more users based on the one or more exogenous variables that independently influence the causal relationships without being affected by feedback from the users