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Track D · Economics & Public Policy

AI in Economics & Public Policy

AI literacy for B.A. / M.A. Economics and Public Policy students at Indian universities — econometrics with ML, policy simulation, AI for development. NEP-aligned discipline module.

Track D · D8

The module you would run on your campus

Economics students complete this module as part of the Track D layer of their existing degree, after the Universal Literacy module (D1).

What AI means in Economics

3 capabilities the module produces.

  • ML-augmented econometrics

    Combine traditional causal-inference methods with modern ML — random forests for heterogeneous treatment effects, double ML, ensemble forecasting.

  • Policy simulation

    Agent-based models and AI-assisted simulation for development and welfare policy. Quantify counterfactuals before they are deployed.

  • Critique of AI policy claims

    Recognise where AI policy recommendations are credible, where they are extrapolations, and where they encode bias.

The module

AI in Economics & Public Policy

D8

AI in Economics & Public Policy

Sem 5–63 credits

Skill outcomes

  • Combine econometric methods with machine learning techniques
  • Run policy simulations using AI-driven models
  • Apply AI to development economics and impact evaluation
  • Critique AI-driven policy recommendations for bias and validity

Prerequisites: D1

Updated 2026-04

Industry context

Indian government and multilateral policy is increasingly informed by AI-augmented analysis. Graduates who can both run that analysis and credibly critique it shape policy more than those who can only do one.

Student outcomes

  • Combine an econometric method with an ML technique on a real dataset
  • Run a policy simulation and present results to a non-technical reviewer
  • Critique an AI-driven policy recommendation for bias and validity
  • Apply AI methods to a development-economics impact-evaluation question

Run this on your campus

Economics students get the same Skill Score as every other Kompas student. Discipline-relevant. Cohort-comparable.