Data Sources Integrated
4 fixtures
Relationships Discovered
4
Policies Evaluated
1 scenario
Knowledge Graph Nodes
12
Data Integration & Knowledge Base Core
Combining diverse data sources to build a unified view for analysis.
Data Integration: The process of combining data from different sources and providing users with a unified view. This is foundational for building a comprehensive knowledge base.
Discovered Relationships & Patterns
Examples of insights derived from analyzing connections within the integrated data.
Relationship Discovery: AI algorithms identify meaningful connections and patterns between entities (e.g., members, policies, transactions) that might not be obvious through manual analysis.
Illustrative Association Comparison
Assessing the impact of specific policies using the integrated knowledge.
The synthetic affected cohort rises after the fixture event; this comparison does not establish causation.
Causal Inference: Statistical methods used to determine cause-and-effect relationships. Applied here to evaluate how policy changes (cause) affect member behaviors or scheme outcomes (effect).
EPFO: Evidence-Based Policy Formulation
- Gain a holistic understanding of how different factors interact to influence policy outcomes.
- Simulate potential impacts of new policies with greater accuracy using the rich knowledge base.
- Identify unintended consequences of past policies.
- Improve the targeting and effectiveness of interventions.
- Enhance transparency and accountability in policymaking.
Research: Advancing Policy Evaluation Methodologies
- Develop novel data integration techniques for complex, heterogeneous social security data.
- Apply advanced relationship discovery algorithms (e.g., from graph theory, NLP) to policy-related data.
- Refine causal inference methods for observational data in public policy contexts.
- Build comprehensive knowledge graphs for social security systems.
- Publish research on the construction and application of knowledge bases for policy impact assessment.