Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI-Powered Econometric Forecasting & Causal Inference bridges two traditions: the predictive power of machine learning and the causal rigour of econometrics. You build forecasting models for economic and financial series, then move to the harder and more valuable question — estimating causal effects. The course covers the modern toolkit for this: difference-in-differences, instrumental variables, and machine-learning approaches like double/debiased ML and causal forests. Throughout, the emphasis is on the distinction that trips up most analysts: prediction versus causation. You finish able to forecast and to estimate a defensible causal effect. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course blends econometrics with machine learning — forecasting economic series and, crucially, estimating causal effects rather than mere correlation.
📋 Course Objectives
1. Forecast economic and financial time series.
2. Distinguish prediction from causal estimation.
3. Apply difference-in-differences and instrumental variables.
4. Use double/debiased ML and causal forests.
5. Interpret and defend causal estimates.
👥 Who Should Enroll?
• Economists and econometricians
• Data scientists in policy and business
• Quantitative social-science researchers
• Students specialising in causal inference
🚀 Key Learning Outcomes
• The ability to forecast and estimate causal effects.
• A causal-inference analysis project.
• A rigorous prediction-versus-causation mindset.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Module 1 – Data Architecture & Causal ML Foundations
Construct multi‑source panel datasets from World Bank and macro‑economic indicators • Implement Multiple Imputation by Chained Equations (MICE) for missing macro data • Engineer temporal and policy‑related features for causal analysis
Module 2 – Advanced Causal Inference Techniques
Apply Double Machine Learning (EconML) to estimate treatment effects • Build Causal Forest models to uncover heterogeneous regional impacts • Execute Synthetic Control using Bayesian Structural Time Series for policy comparison
Module 3 – Model Diagnostics & Robustness
Conduct placebo tests and falsification checks • Analyse SHAP values for interpretability • Validate assumptions with balance and overlap diagnostics
Module 4 – Policy Simulation & Dashboarding
Create interactive policy simulation tools in Streamlit • Generate counterfactual scenario analyses • Design publication‑grade visualizations with Plotly
Module 5 – Reproducible Research Workflow
Structure code notebooks for reproducibility • Export results to LaTeX/Word for journal submission • Version‑control datasets and scripts with Git
Module 6 – Communication & Impact Reporting
Craft policy briefs that translate causal findings into actionable recommendations • Prepare presentation decks for academic and governmental audiences • Develop grant‑proposal sections showcasing methodological rigor
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | EconML |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Statsmodels |
| Covered Tool / Platform | CausalML |
| Covered Tool / Platform | MICE |
| Covered Tool / Platform | Streamlit |
| Covered Tool / Platform | Plotly |
| Covered Tool / Platform | Git |
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