S.C.A.L.E
Structural Causal Analysis of Labor & Education
Causal Modeling | System Dynamics | Policy Evaluation | Computational Economics
Overview
S.C.A.L.E. is an integrated computational policy-analysis framework developed to study the long-term consequences of engineering education expansion in India. Rather than treating higher education, labor markets, and economic growth as independent systems, S.C.A.L.E. models them as a single dynamic ecosystem connected through delayed feedback loops, causal dependencies, and heterogeneous agent behavior. The framework combines System Dynamics (SD), Structural Causal Models (SCM), and Agent-Based Modeling (ABM) to evaluate how policy interventions influence graduate employability, underemployment, wage dynamics, migration, human-capital retention, and economic productivity over long time horizons.
Key Features
Multi-Layer Causal Modeling
Integrated three paradigms: System Dynamics (macro), Structural Causal Modeling (causal inference), Agent-Based Modeling (individual decisions).
Long-Term Policy Simulation
Evaluates education policies over 15-year horizons accounting for delayed pipelines, labor-market adjustment, and nonlinear migration.
Counterfactual Policy Evaluation
Implements Pearl's do-calculus for policy regime simulation: Business-as-Usual, Seat Cap, Quality-First, Migration Friction, Optimal Mix.
Historical Calibration
Calibrated using longitudinal datasets (2010–2023): AICTE, AISHE, PLFS, RBI, CMIE, OECD, Ministry of Statistics.
Structural Causal Discovery
Estimated ATE = 0.4482: 1pp increase in enrollment share → ~44.8pp increase in graduate unemployment.
Technology Stack
Algorithms & Methods
System Dynamics
Continuous stock-flow differential equations modeling enrollment, employment, GDP, wages.
Structural Causal Model (SCM)
DAG-based causal modeling with Pearl's do-operator for counterfactual interventions.
Agent-Based Model (ABM)
Individual graduate agents with heterogeneous decision rules: reservation wage, migration threshold, visa probability.
Monte Carlo Simulation
Stochastic simulation of policy scenarios with confidence intervals.
System Architecture
System Dynamics Engine
Macro-level stock-flow equations for enrollment, employment, GDP, wages
SCM Layer
Causal graph construction, do-calculus interventions, ATE estimation
ABM Layer
Individual agent simulation with heterogeneous decision rules
Calibration Module
Historical data fitting against AICTE, AISHE, PLFS, RBI datasets
Policy Simulator
Five-regime policy evaluation with comparative metrics
Results & Outcomes
- Published in IEEE Access (June 2026)
- Developed integrated SD–SCM–ABM framework for education policy evaluation
- Demonstrated causal links between seat expansion and graduate employability
- Optimal Integrated Policy Mix: 71.37% unemployment reduction, 1.31 GDP Index
- Quality-First Reform consistently outperformed simple seat-cap regulation
Publication
Unbalanced Expansion of Engineering Education in India: A Data-Driven Policy Analysis
Journal: IEEE Access
DOI: 10.1109/ACCESS.2026.3704923