- Design defensible causal research frameworks for policy impact evaluation.
- Apply advanced causal machine learning methods to estimate treatment effects.
- Analyze heterogeneous policy impacts using large-scale panel data.
- Develop reproducible, Python-based econometric workflows.
- Produce publication-ready outputs and policy simulation models for evidence-based decision-making.
Mentor Based
AI-Powered Econometric Forecasting & Causal Inference
Advancing Rigorous Policy Evaluation Through Causal Machine Learning
About This Course
This intensive 3-day workshop explores the Causal AI for Policy & Economic Forecasting is an advanced workshop focused on applying modern causal machine learning techniques to rigorous policy impact evaluation.
Participants will learn to integrate econometric theory with methods such as Double Machine Learning, Causal Forests, and Synthetic Control models to estimate credible treatment effects from observational data. The program emphasizes robust empirical design, reproducible workflows, and publication-ready outputs.
Designed for PhD scholars, faculty, and policy researchers with prior knowledge of econometrics and Python.
Aim
To equip researchers with AI-enhanced causal econometric methods for rigorous policy impact evaluation and evidence-based economic forecasting.
Workshop Objectives
- To strengthen understanding of causal inference principles in empirical policy research.
- To apply advanced methods such as Double Machine Learning and Causal Forests for treatment effect estimation.
- To implement Synthetic Control techniques for comparative policy evaluation.
- To construct and manage large-scale panel datasets for economic analysis.
- To develop reproducible, publication-ready outputs for policy impact assessment and forecasting.
Workshop Structure
📊 Day 1 — Data Architecture & Causal ML Foundations
- Focus: Building rigorous empirical foundations for economic and policy analysis
- Foundations of causal inference in observational economic research; panel data structures and identification challenges; missing macroeconomic data and Multiple Imputation by Chained Equations (MICE)
- Hands-On:
- Constructing and harmonizing multi-source panel datasets (World Bank & macroeconomic indicators)
- Implementing MICE for macroeconomic datasets in Python
- Feature engineering for temporal and policy-related financial indicators
💹 Day 2 — Advanced Causal Inference for Policy & Market Impact
- Focus: Estimating credible and heterogeneous economic policy effects
- Theoretical foundations of Double Machine Learning (DML), Causal Forests for heterogeneous treatment effects, and Synthetic Control using Bayesian Structural Time Series
- Hands-On:
- Implementing DML for treatment effect estimation using EconML
- Building and interpreting Causal Forest models for regional economic impact
- Applying Synthetic Control methods for policy comparison analysis
- Conducting robustness checks and model diagnostics
📈 Day 3 — Policy Simulation & Publication-Ready Financial Analytics
- Focus: Translating causal findings into actionable economic insights
- Best practices for reporting heterogeneous effects; interpreting SHAP values; communicating counterfactual results to academic and policy audiences
- Hands-On:
- Building interactive economic policy dashboards using Streamlit
- Developing counterfactual policy simulation tools
- Generating publication-grade coefficient plots and economic trend visualizations using Plotly
- Exporting reproducible outputs suitable for high-impact journals and grant proposals
Who Should Enrol?
- PhD scholars, postdoctoral researchers, and faculty in Economics and Public Policy
- Policy analysts and research professionals in government or think tanks
- Quantitative researchers and data scientists working with economic data
- Advanced postgraduate students with prior knowledge of econometrics and Python
Important Dates
Registration Ends
02/28/2026
IST 4 : 30 PM
Workshop Dates
02/28/2026 – 03/02/2026
IST 5: 30PM
Workshop Outcomes
Fee Structure
Student
₹2499 | $75
Ph.D. Scholar / Researcher
₹3499 | $85
Academician / Faculty
₹4499 | $95
Industry Professional
₹6499 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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