Abstract: Estimator mechanisms for automated computer causal effect estimation are provided. An input dataset is received that includes an initial set of covariate data. An estimation of the relevance of covariates in the initial set is performed where relevance is to one or more causal effect relationships between a given at least one action and an outcome. Based on results of the execution of the estimation, a subset of the initial set of covariates is determined that are covariates relevant to one or more causal effect relationships. A modified dataset, comprising the subset of relevant covariates and at least a portion of the input dataset is generated. The modified dataset is input to a causal effect estimator that processes the modified dataset to generate causal effect relationship estimates for specifying causal effects between the given set of actions and the outcome.

Abstract: A system may predict costs for a set of members by building and using a predictive pipeline. The pipeline may be built using a set of historical data for training members. A set of member-level features can be identified by performing empirical testing on the set of historical data. The trained configurable predictive pipeline can generate a set of predictive data for each member, using historical test data for a set of testing members. The system can then generate a predictive report for each set of predictive data.