This course is for professionals and researchers from all academic disciplines who wish to improve their use of Stata.
The course assumes limited statistical knowledge and only some experience of using statistical software. The participants will be introduced to Stata and causal inference using practical examples.
The fundamentals of data analysis and visualization will also be taught, using appropriate Stata commands and programming techniques. Then, the participants will be introduced to the main data analysis tools related to regression, including linear and logistic regression, local polynomial regression, and related machine learning approaches.
Participants will apply these methods to specially chosen datasets using data from economics and medical research. Participants will learn about quasi-experimental methods (via simulation of bias and variance properties, and hands-on examples) for propensity score matching and reweighting, panel methods including difference-in-difference designs, instrumental variables, and regression discontinuity.
|9am-11am(Eastern Standard Time)
||12pm-2pm (Eastern Standard Time)
Causation, Experiments, and Selection on Observables:
- Causal inference, and the unique advantage of randomization.
- Running regressions and visualizing impacts in experiments.
- Correct standard errors. Simulation versus asymptotic results. Programming in Stata.
- Regression adjustment in quasi-experimental settings.
- Propensity score matching and reweighting in theory and practice.
Advanced Quasi-Experimental Methods:
- Difference-in-difference designs, fixed effects, and mixed models.
- Testing assumptions and decomposing sources of variation.
- Instrumental variables in theory and practice.
- Weak instruments, invalid instruments, and other disasters.
- Regression discontinuity and kink designs.
- Extrapolation, mediation, and other unsolved problems.
Principal texts for pre-course reading
Principal texts for post-course reading
It would be helpful but not necessary to already have some basic knowledge of opening a dataset in Stata, running simple commands, editing a do file in a text editor. We will assume some prior knowledge of statistics, including experience conducting a hypothesis test, but no prior training is required.
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. Contact us for more information.
- 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.
- Full payment of course fees is required prior to the course start date to guarantee your place.
- Registration closes 1 calendar day 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.
The number of seats available is restricted. Please register early to guarantee your place.