IEG Winter School 2026
IEG’s Winter School 2026
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Introduction
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Course Details
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Outcomes
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Instructors
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Application Process & Fees
We are excited to organize IEG’s Winter School on causal Inference from November 16 to November 27, 2026. Most social science research is driven by causal questions. However, conducting randomized control trials in these domains is not always feasible due to ethical or practical concerns. As a result, quasi-experimental designs based on observational data are increasingly being used to estimate causal effects.
The Winter School aims to provide participants with conceptual knowledge and the practical skills required for applying such quasi-experimental causal inference methods. The course will include examples using real-world data and hands-on sessions in Stata so that participants can understand and apply these approaches in their research. It will be conducted by IEG’s nationally and internationally reputed faculty.
A globally renowned economist will deliver a Keynote lecture. The previous Keynote Lectures were delivered by Prof. Rohini Somnathan, Prof. J.V. Meenakshi, Prof. Farzana Afridi , Prof. Abhiroop Mukhopadhyay and Prof. Achin Chakraborty
Additionally, shortlisted applicants for the Winter School will have the opportunity to present their ongoing research work. Although the presentation is optional, we strongly encourage all participants to take advantage of this opportunity to receive valuable feedback from IEG faculty, external experts, and other attendees at the IEG Winter School. Participants are expected to present research that applies any causal inference technique. Alternatively, they may present their empirical work and discuss potential extensions using causal inference methods. One outstanding paper will receive the Best Paper Award.
Target audience
Research scholars and early career researchers in academia, corporate, government, and non-profit organizations.
Details regarding the structure of the Winter School will be shared with those shortlisted.
For any further clarifications, please contact us at learning@iegindia.org
Pre-requisites
- Master's Degree in Economics or other related fields
- Knowledge of post-graduation level econometrics
- Basic Understanding of Stata software
Format
- 32 hours of in-person modules with practical sessions and special lectures spread over the first eight working days
- A keynote lecture by a globally recognized economist
- Presentations by participants at the end of Winter School
Course Content
Introduction to Causal Inference I
- Theory of Probability
- Theory of Linear Regression
- Panel Data Regression
- Logit and Probit Models
- Discussion of published research papers and applied exercises using Stata
Introduction to Causal Inference II
- Understanding the concept of Causal Inference
- Potential Outcomes Framework
- Observational Data versus Experimental Data
- Discussion of published research papers and applied exercises using Stata
Directed Acyclic Graphs (DAGs) for Causal Inference and Instrumental Variables
- Rationale for Graph and basic elements of causal graphs
- Causation, Confounding and Selection bias using DAGs
- Collider and Backdoor Criterion, Collider Bias
- DAG with a simple regression
- Instrumental Variables
- The problem of endogeneity in the regression model
- Endogeneity test
- Two-Stage Least Squares Regression
- Discussion of published research papers and applied exercises using Stata
Propensity score matching (PSM)
- Introduction to Propensity Score Matching (PSM)
- Conducting PSM
- Matching Methods: Strengths and Limitations of PSM
- Discussion of published research papers and applied exercises using Stata
Endogenous Switching Regression
- Introduction to endogenous switching regression
- Why OLS regression fails under endogenous regime choice
- Difference between treatment effect and selection effect
- Counterfactual framework
- Econometric structure of ESR
- Interpretation of ρ (Rho)
- ESR comparison with alternative approaches (2SLS & PSM)
Regression Discontinuity Design (RDD)
- Introduction to RDD, Fuzzy vs Sharp RDD
- Specification Tests and Sensitivity Analysis
- Limitations of RDD
- Discussion of published research papers and applied exercises using Stata
Difference in Differences (DiD)
- Basics of DiD
- 2x2 DiD set up
- Assumptions for DiD
- Discussion of published research papers and applied exercises using Stata
Pedagogy
The workshop will adopt a highly interactive pedagogy and use technology to create an engaging learning experience. Activities will include expert-led lectures, in-depth discussions of published research papers, and hands-on practice with Stata & R software to develop a practical understanding of causal inference techniques.
Course Outcomes
On successful completion of this course, students will be able to:
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- Understand the principles of causal inference
- Use causal inference techniques to evaluate the effect of various programmes on outcomes.
- Model causal relations in an econometric/statistical framework
- Acquire software and technical skills to pursue research on causal inference
Certification
The Institute of Economic Growth shall issue a Certificate of Participation (CoP) upon fulfilment of 100 percent attendance criteria and other parameters. Those who present their ongoing research work will get a separate certificate of presentation.
Chair of The Winter School
Prof. Sabyasachi Kar (Director, IEG)
Chair of Course Committee
Dr. M. Rahul
Dr. Srishti Gupta
Dr. Mulla Areef
Course Committee
Dr. Archana Dang
Dr. Gautam Kumar Das
Dr. Parma Chakravartti
Dr. Sandhya Garg
Dr. Sukhdeep Singh
Instructors
Prof. Vikram Dayal
Prof. Saudamani Das
Prof. C.S.C Sekhar
Dr. William Joe
Dr. Oindrila De
Dr. Archana Dang
Dr. Gautam Kumar Das
Dr. M Rahul
Dr. Sukhdeep Singh
Dr. Parma Chakravartti
Dr. Srishti Gupta
Dr. Mulla Areef
Dr. Sandhya Garg
Dr. Sunaina Dhingra
Fee Structure
The enrolment fee for the course is Rs. 3,000 for all participants. In addition, the tuition fee is Rs. 7,000 per student and Rs. 15,000 per working professional; this includes lunch on all working days.
For participants opting to stay on the IEG campus, accommodation will be available at an additional cost of Rs. 8,000 per person, which includes breakfast and dinner for the duration of the workshop. Due to limited availability, accommodation will be allotted on a first-come, first-served basis. Preference for campus accommodation will be given to participants from outside Delhi.
Shortlisted applicants are required to pay the course fee including enrolment, tuition and accommodation charges (if applicable) within the stipulated time upon selection.
Fee Structure
| Enrolment Fees | Tuition Fee | Accommodation Charges | |
| Students | 3000 INR | 7000 INR | 8000 INR |
| Others | 3000 INR | 15000 INR | 8000 INR |
Registration and Selection
Due to the highly interactive nature of the course, enrolment will be limited to 20 participants. Applications must be submitted by 10th October, 2026. Applicants are advised to use a Gmail account while filling out the application form to ensure smooth communication.
Participants will be selected based on their academic background and motivation to successfully complete the program. Shortlisted candidates will be notified via email by 16th October, 2026. To confirm their participation, selected candidates must pay the total course fee by 30th October, 2026. Failure to do so within the stipulated deadline will result in the offer being extended to candidates on the waiting list. The applicant must upload their master’s marksheet to the Google Form as proof of minimum qualification.
All applications will be processed strictly on the basis of the information and documents submitted by the applicants. In the event that any information or document is found to be false or misleading, whether by omission or commission, the application is liable to be rejected at any stage of the selection process.