Recent developments in causal inference and program evaluation using Stata

Cass Business School 2 days Stata Intermediate
Delivered by: Dr. Giovanni Cerulli

COURSE DATES: 14th - 15th May 2018

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 “binary” treatment setting poses limitations.

More specifically, the course will focus on these approaches:

  • (i) Difference-in-differences (DID) with time-varying 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; and, finally
  • (iv) causal inference with continuous treatment (namely, dose-response models)

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.

Course Agenda

Day 1

Session 1: Econometrics of program evaluation: an overview

  • Causal inference and program evaluation
  • Observable vs. unobservable selection
  • Statistical background for the binary treatment setting
  • Assumptions and notation: Conditional independence and endogeneity
  • Review of the econometric methods for binary treatment
  • Limits of the binary treatment setting and new developments

Session 2: Difference-in-differences (DID) with time-varying binary treatment

  • Standard DID: statistical setting and conceptualization
  • DID with panel data and repeated cross-section
  • DDID: Difference-in-differences with time-varying binary treatment
  • DDID application on real data using the Stata command: ddid

Session 3: DID with many times and locations: the Synthetic-Control Method (SCM)

  • SCM: statistical setting and conceptualization
  • Parametric SCM and Stata implementation via synth
  • Nonparametric SCM and Stata implementation via npsynth

Day 2

Session 1: Multivalued and quantile treatment effect estimation

  • Causal inference with multivalued treatment
  • Statistical setting and estimation under conditional independence
  • Application using the Stata command poparms
  • Estimation of quantile treatment effect under conditional independence

Session 2: Causal inference with continuous treatment: dose-response models

  • The logic of dose-response models
  • The Generalized Propensity Score (GPS) approach
  • Application of the GPS via the Stata commands gpscore and doseresponse
  • Regression-Adjustment based dose-response models (RADR)
  • Application of the RADR via the Stata command ctreatreg

Learning Ratio: 30% Theory, 30% Demonstration and 40% Practical

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


  • 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

Terms & Conditions

  • 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.
  • Cost includes course materials, lunch and refreshments.
  • Attendees are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course.
  • If you need assistance in locating hotel accommodation, please notify us at the time of booking.
  • Payment of course fees required prior to the course start date.
  • Registration closes 5-calendar days prior to the start of the course.
    • 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.

The number of seats available is restricted. Please register early to guarantee your place.

  •  CommercialAcademicStudent
    May (14/05/2018 - 15/05/2018)

All prices exclude VAT or local taxes where applicable.

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