2nd Summer School on causal Inference
2nd Summer School on causal Inference
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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
The Institute of Economic Growth (IEG) is pleased to announce its Summer School on Causal Inference, scheduled from July 13 to July 24, 2026.The program is designed to provide participants with a rigorous conceptual foundation and the practical skills required to conduct causal inference using quasi-experimental methods in empirical research.
Contemporary social science research is fundamentally driven by questions of causal inference. Although randomized controlled trials (RCTs) are widely regarded as the benchmark for causal inference, their implementation is often constrained by ethical, institutional, and logistical considerations, as well as substantial financial requirements. These limitations necessitate the adoption of alternative empirical strategies that support credible causal inference in non-experimental settings.
In this context, quasi-experimental approaches based on observational data have become central to applied causal inference. The course will therefore emphasize both the theoretical foundations and practical implementation of these methods. It will incorporate empirical applications using real-world datasets, complemented by hands-on training in Stata and R, to enable participants to effectively conduct causal inference in their own research.
Consistent with the program’s emphasis on applied causal inference, shortlisted applicants will have the opportunity to present their ongoing research. While participation in this component is optional, it is strongly encouraged, as it provides a valuable platform for receiving critical, constructive feedback from IEG faculty, external experts, and fellow participants. Submissions are expected to engage substantively with causal inference, either by applying established methodological approaches or by explaining how causal inference techniques may be integrated into ongoing empirical analyses. This component is designed to facilitate scholarly exchange and strengthen participants’ ability to rigorously implement causal inference methods in their research. One outstanding paper will be selected for the Best Paper Award.
Target audience
Students, professionals, and research scholars in academia, government officials, professionals from non-profit organizations, as well as individuals from related fields.
For any further clarifications, please contact us at learning@iegindia.org
Pre-requisites
- Post-graduation in economics or any other subject, having advanced knowledge of econometrics/statistics.
- Basic understanding of Stata/R software.
- A Statement of Purpose (SOP) of at least 150 words is required at the time of application. The SOP should explain why the applicant plans to attend the Summer School and how it will contribute to the applicant’s future research activities.
Course Details
- 34 hours of in-person modules with theory and hands-on sessions
- A keynote lecture by a prominent economist.
- Participant presentations and feedback at the end of the programme.
Course Content
Introduction to Basic Econometrics, Stata, and R
- Theory of Probability
- Theory of Linear Regression
- Panel Data Regression
- Logit and Probit Models
- Introduction to software: Stata & R
Introduction to Causal Inference
- Understanding the concept of Causal Inference
- Potential Outcomes Framework
- Observational Data versus Experimental Data
Directed Acyclic Graphs (DAGs) for Causal Inference and Instrumental Variables (IV)
- 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
- Structural Causal Models (SCM)
- The problem of endogeneity in the regression model
- Instrumental Variables (IV)
- Two-Stage Least Squares Regression (2sls)
- Endogeneity test
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)
Propensity score matching (PSM)
- Introduction to Propensity Score Matching (PSM)
- Conducting PSM
- Matching Methods: Strengths and Limitations of PSM
Regression Discontinuity Design (RDD)
- Introduction to RDD, Fuzzy vs Sharp RDD
- Specification Tests and Sensitivity Analysis
- Limitations of RDD
Difference in Differences (DiD)
- Basics of DiD
- 2x2 DiD set up
- Assumptions for DiD
Survey Inference
- Descriptive vis-à-vis causal questions with survey data using DAGs and simulation.
- Understanding adjustment weighting for sample selection and non-response bias.
- Estimating sample and population average treatment effects.
Pedagogy
The programme follows a highly interactive approach, combining
- Expert-led lectures
- Hands-on training in Stata & R
- Research paper discussions
Course Outcomes
- Understand the principles of causal inference
- Apply causal methods to real-world problems
- Build econometric models for causal analysis
- Acquire software and technical skills to pursue research on causal inference
Certification
The Institute of Economic Growth will issue a Certificate of Participation upon fulfillment of the 100 percent attendance requirement and other specified criteria. Participants who present their papers at the Summer School will receive a separate Certificate of Presentation.
Chair of the Summer School
Prof. Sabyasachi Kar (Director, IEG)
Course Committee
Prof. C.S.C Sekhar
Prof. Vikram Dayal
Dr. Archana Dang
Dr. Gautam Kumar Das
Dr. M. Rahul
Dr. Parma Chakravartti
Dr. Sandhya Garg
Dr. Srishti Gupta
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. Parama Chakravartti
Dr. Srishti Gupta
Dr. Mulla Areef
Dr. Sunaina Dhingra
Fee Structure
The enrolment fee for the course is Rs. 3,200 for all participants. In addition, the tuition fee is Rs. 7,000 per student and Rs. 12,000 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. 7,800 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 compulsory, non-refundable enrolment fee of Rs. 3,200 at the time of confirmation. The remaining balance must be paid closer to the commencement of the Summer School.
Fee Structure
| Enrolment Fees | Tuition Fee | Accommodation
Charges |
Total | |
| Students | 3200 INR | 7000 INR | 7800 INR | 18000 INR |
| Others | 3200 INR | 12000 INR | 7800 INR | 23000 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 15th May, 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 31st May, 2026. To confirm their participation, selected candidates must pay a non-refundable enrolment fee of Rs. 3,200 by 7th June, 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 degree certificate/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.