Advances in Causal Inference and Program Evaluation using Stata

Econometric modelling for causal inference and program evaluation have witnessed a tremendous development in the last decade, with new approaches and methods addressing an expanding set of challenging problems, both in medical and the social sciences. This course covers some recent developments in causal inference and program evaluation using Stata.

This live and online short course will cover:

• The essential tools, both theoretical and applied for a proper use of recent micro-econometric methods for policy evaluation and casual modelling, in situations where the standard treatment set poses limitations.
• Difference-in differences (DID), with time varying and time-fixed binary treatment.
• The Synthetic Control Method (SCM), for program evaluation, suitable when datasets, on many times and locations, are available.
• Models for multivalued and quantile treatment effect estimation.

Mastery of Recent Micro-Econometric Methods: 

• Gain a deep understanding of recent developments in causal inference and program evaluation within the last decade. 
• Master essential tools, both theoretical and applied, for utilizing micro-econometric methods in policy evaluation and causal modeling. 

Proficiency in Advanced Approached

•  Acquire expertise in Difference-in-Differences (DID) with both time-varying and time-fixed binary treatment settings.
• Learn to apply the Synthetic Control Method (SCM) for program evaluation, particularly when dealing with datasets spanning multiple times and locations.
• Develop skills in models for multivalued and quantile treatment effect estimation. 

Practical Application and Interpretation: 

• Gain hands-on experience in setting up and managing a correct evaluation design using Stata.
• Learn to identify the appropriate econometric method based on the policy framework and interpret results accurately.
• Apply learned concepts to real datasets through various instructional examples. 

On completion of this course, you will be able to set up and manage a correct evaluation design using Stata, by identifying the policy framework and the appropriate econometric method to interpret correctly the results.

You will receive a signed certificate of as proof of professional development upon completion of this course.

• Informed Decision-Making in Various Domains: Participants will be empowered to apply machine learning techniques in diverse fields, such as social sciences, economics, and health. This knowledge will enable them to make informed decisions based on insights extracted from large datasets.
• Enhanced Research Capabilities: Researchers can apply the learned techniques to enhance their research methodologies. The course's focus on correct model specification and model-free classification ensures robust analysis, contributing to the reliability of research findings.
• Efficient Data Utilization: Professionals and policymakers will benefit from the ability to extract valuable information from large and possibly noisy datasets. This efficiency in data utilization can lead to improved policy formulation, strategic planning, and business decision-making.

• This course is tailored for researchers, analysts, and academics working in fields like economics, social sciences, or finance. Familiarity with basic econometrics is helpful, but no prior experience with panel data methods or Stata is required.

It is preferable but not strictly needed to have attended the course “Econometrics of program evaluation using Stata”. It is also required some knowledge of basic econometrics: notion of conditional expectation and related properties; point and interval estimation; regression model and related properties; probit and logit regression.

Basic knowledge of the Stata software

Principal texts for pre-course reading:

• Cerulli, G. (2015), Econometric Evaluation of Socio-Economic Programs: Theory and Applications, Springer.
• Wooldridge, J.M. (2010). Econometric Analysis of cross section and panel data. Chapter 21. Cambridge: MIT Press.

Principal texts for post-course reading:

• Abadie, A., Diamond, A., and Hainmueller, J. (2010), Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program, Journal of the American Statistical Association, Vol. 105, No. 490, 493-505.
• Bia, M. and Mattei, A. (2008), A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score, Stata Journal, Volume 8, Number 3.
• Cattaneo, M., Drukker, D., and Holland, A. (2013), Estimation of multivalued treatment effects under conditional independence, Stata Journal, Volume 13, Number 3.
• Cerulli, G. (2015), ctreatreg: Command for fitting dose-response models under exogenous and endogenous treatment, Stata Journal, Volume 15, Number 4.

• Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
• Additional discounts are available for multiple registrations.
• Temporary, time limited licences for the software(s)  used in the course will be provided. You are required to install the software provided prior to the start of the course.
• Payment of course fees required prior to the course start date.
• Registration closes 5-calendar days prior to the start of the course.

Cancellations or changes to your registration

• 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
• 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
• No fee returned for cancellations made less than 14-calendar days prior to the start of the course.