Abstract: A method of identifying causal relationships includes receiving data comprising a set of values corresponding to one or more variables, and generating a list of candidate causal models of relationships between or within the variables. The list is ranked based on a likelihood of each candidate causal model, wherein the likelihood includes at least a correlation value. The method further includes receiving feedback identifying a candidate causal model and a change in rank of the candidate causal model, re-ranking the list based on the feedback, and displaying the re-ranked list. The method generates an intervention comprising a suggested modification corresponding to a variable of a selected causal model among the candidate causal models in the re-ranked list, receives additional data corresponding to the variable of the suggested modification and evaluates the additional data to determine whether the likelihood of the selected causal model has changed.

Abstract: A method for calculating generalized utilities and choice predictions is described. The method includes identifying an individual's choice a user desires to predict and relevant parameters influencing the individual's choice. The method also includes manually selecting between different function forms and parameter estimates for an expected generalized utility (EGU) model if a choice data is unavailable. The method further includes providing a machine learning (ML)-based recommendation for the function forms and parameter estimates if the choice data is available. The method also includes displaying a predicted choice as well as a confidence interval associated with the predicted choice estimated using the EGU model.

Abstract: A method for monitoring user decision making activity is described. The method includes logging a user decision and decision communications corresponding to the user decision. The method also includes identifying the user decision as a compromised user decision based on an emotional status of a user determined from the decision communications. The method further includes determining a subsequent emotional status of the user based on a subsequent user communication corresponding to the compromised user decision. The method also includes providing an advice recommendation to the user when a degraded emotional status is detected regarding the compromised decision.

Abstract: A method of identifying causal relationships includes receiving data comprising a set of values corresponding to one or more variables, and generating a list of candidate causal models of relationships between or within the variables. The list is ranked based on a likelihood of each candidate causal model, wherein the likelihood includes at least a correlation value. The method further includes receiving feedback identifying a candidate causal model and a change in rank of the candidate causal model, re-ranking the list based on the feedback, and displaying the re-ranked list. The method generates an intervention comprising a suggested modification corresponding to a variable of a selected causal model among the candidate causal models in the re-ranked list, receives additional data corresponding to the variable of the suggested modification and evaluates the additional data to determine whether the likelihood of the selected causal model has changed.