Structural Equation Models and Causal Inference
using Stata

Advanced training

6-9 Set 2016, ISEG, Lisbon


Most questions of interest are fundamentally questions of causality, i.e. what is the effect of some variable
x on some other variable y. This course introduces the statistical methods that are currently available for
studying such questions. Modern causal analyses are based on either the potential outcomes framework or
the structural equation framework. Advantages and disadvantages of both frameworks will be discussed.
Rubin developed the potential outcome approach into a powerful formal framework, the Rubin Causal Model
(RCM), for assessing causation in observational data. The resulting methods make only few assumptions,
which adds credibility to the results obtained.

Structural Equation Models (SEM) make more assumptions, and have been a dominant influence in management
science and marketing for decades. More recently, it has captured the interest of the other social sciences.
A Structural equation model is a causal framework that provides elegant and relatively easy-to-use methods
to deal with many important issues that applied empirical researchers have to face. These include multi-level
random effects, endogeneity, sample selection and missing data.

Recent versions of Stata include implementations of these methods. This course aims to introduce the theory
of the RCM and SEM and show how to apply the theory using Stata.

Course Instructors:

Pierre Hoonhout


Pierre Hoonhout (Universidade de Lisboa)

Target audience

This event is addressed to students, professionals and researchers in the social sciences that have a basic understanding of the most commonly used econometric models.


For more information please contact us.


CEMAPRE - Centre for Applied Mathematics and Economics

Rua do Quelhas, n.º 6
1200-781 Lisboa

Tel: (+351) 213 925 876