| 摘要 |
Systems, methods, and articles of manufacture are disclosed for the identification and management of causal knowledge. Organizations can use this knowledge to improve performance by, for example, designing cost-effective interventions to change customer or employee behavior. These methods use novel ways to abstract, standardize, and automate the identification and management of causal knowledge, thus making it accessible and affordable to most business users. Moreover, methods are disclosed that—for the first time—solve two critical problems of randomized controlled trials: Missing data on the outcomes of interest, and the inability to generalize findings from the experimental sample to the population using non-probability samples. This includes solving a fundamental problem (present also in probability samples) with the generalization of segmented analysis from a study sample to a population. Use of these embodiments will make the identification and management of causal knowledge much more cost effective, efficient, and reliable. |
| 主权项 |
1. A system for integrated identification and management of causal knowledge, the system comprising:
a knowledge identification and management engine comprising a knowledge discovery graph (KDG), wherein the KDG presents information of an organization's existing causal knowledge within a domain of the KDG, wherein the KDG additionally functions as a graphical user interface to provide access to a graphical knowledge database, wherein the graphical knowledge database stores relevant disembodied information about the causal knowledge represented by the KDG; a knowledge market configured to be accessible by one or more networks, the knowledge market having a single information architecture and/or related application programing interfaces; and a computing device configured to receive a selection of a research goal and target population of interest in association with the KDG, wherein the computing device if further configured to request qualitative or quantitative causal knowledge about direct causes and indirect causes of an outcome of interest including which causes in the KDG or the graphical knowledge database may be directly manipulable, indirectly manipulable, or non-manipulable, wherein the computing device is further configured to request qualitative or quantitative causal knowledge about costs and effectiveness of intervention to change some direct or indirect cause of the outcome of interest, wherein the computing device is further configured to request knowledge about variables that may d-separate the attrition and the outcome being investigated, including causes in common, wherein the computing device is further configured to aggregate results received in response to the request for knowledge about direct and indirect causes, the request for knowledge about costs and effectiveness, and the request for knowledge about variables that may d-separate into the KDG using one or more aggregation modules provided by the knowledge identification and management engine or the knowledge market, wherein the computing device is further configured to measure variables in the KDG or graphical knowledge database, including measuring possible causes of the outcome of interest and attrition, wherein the computing device is further configured to add measurements from one or more other databases to variables in the KDG or graphical knowledge database, wherein the computing device is further configured to generate a design for a generalizable randomized controlled trial (RCT) to test aspects of the KDG, the generation of the design including selection of sampling plans that have known probability of generating causal estimates that are consistent and approximately unbiased for the target population of interest, even when convenience non-probability samples are used, wherein the computing device is further configured to generate a RCT implementation plan, the implementation plan including built-in safeguards against problematic attrition and a Gantt graphical abstraction engine for managing the implementation process, wherein the computing device is further configured to process results of the RCT in connection with the KDG by determining problematic attrition and generalizing segmented or unsegmented findings from the RCT based on a non-random sample from the population to the target population or to a sub-sample thereof, wherein the computing device is further configured to determine whether problematic attrition exists, wherein the computing device is further configured to determine conditions under which a generalization is acceptable, and wherein the computing device is further configured to determine whether the generalization is transportable. |
