Causal Knowledge Graph (Causal KG) for Business and Management
We are constructing the world's largest causal knowledgebase concerning business and management. Built on knowledge graph technologies and graphic data science, we seek to visually represent and query a network of deductive knowledge (e.g., high-quality scientific literature) and inductive knowledge (e.g., cognitive models from seasoned practitioners).
*We are currently examining the policies regarding 'fair use' and 'knowledge IP' before we make our CausalKG publicly accessible.
Demo videos
Load CausalKG from neo4j Aura cloud
Example 1. Query to show all drivers of Economic well-being as the outcome
Example 2. Query to show all drivers of Social well-being as the outcome
Example 3. Query to show common drivers of both Economic and Social well-being as outcomes
Example 4. Query to show all nodes connected with both Performance and Turnover in less than or equal to three links. This helps to find confounders for causal inference.
Example 5. Query to show all effects (consequences) of Narcissistic behavior
Example 6. Query to show the state-of-the-art knowledge by theory perspectives
Example 7. Query to show the state-of-the-art knowledge by a journal list
Example 8. Query to show the state-of-the-art knowledge by sample contexts
Example 9. Query to show inconsistent logics/arguments in the literature, i.e., different predictions for the relationship between the same pair of constructs/variables
Example 10. Query to show the same prediction for the relationship between exactly the same pair of constructs/variables, but framed under different theories
Connect the CausalKG as API with Python to enable causal graph-based data science
Query in Jupyter Notebook to find confounders and instrumental variables for causal modeling
Share this page via
GoPeaks has ceased its business activities since April 2022.
Funded through a hybrid model of grants, gifts, and technology commercialization, GoPeaks is a collection of research and technology development to accelerate and scale knowledge synthesis into real-world decision solutions. It is especially focused on knowledge graph-based prescriptive (causal) analytics. By grounding its solutions to improve multi-stakeholder performance management, its goal is to build a shared world brain serving broad interests of people and society. Please use the contents on the GoPeaks website for non-commercial, educational purposes only.
Subscribe here for updates.
Join Knowledge Integration, Synthesis, and Engineering (KnISE) - a community of synthesizers, builders, and solvers for business and management.