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