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.
It will provide participants with the essential tools, both theoretical and applied, for a proper use of recent micro-econometric methods for policy evaluation and causal modelling in situations where the standard treatment setting poses limitations.
This course is offered in collaboration with Lancaster University
More specifically, the course will focus on these approaches: (i) Difference-in-differences (DID) with time-varying and time-fixed binary treatment; (ii) the Synthetic Control Method (SCM) for program evaluation, suitable when datasets on many times and locations are available; (iii) models for multivalued and quantile treatment effect estimation.
After attending the course, the participant will be able to setting up and managing a correct evaluation design using Stata, by identifying the policy framework, the appropriate econometric method to use interpreting correctly the results. The course will provide various instructional examples on real datasets.
30% Theory, 30% Demonstration and 40% Practical
|Morning Session||Afternoon Session||Q&A with Instructor|
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.
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.
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
The number of attendees is restricted. Please register early to guarantee your place.