Checking date: 20/07/2020

Course: 2020/2021

Causal Inference
Study: Master in Social Sciences (325)

Coordinating teacher: CABRAS , STEFANO

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 6.0 ECTS


Students are expected to have completed
Statistics I Statistics II
Competences and skills that will be acquired and learning results.
Basic competences: 1. Quantitative knowledge giving the opportunity of being original in the proposal and/or application of new studies in a research context. 2. Implementation of the techniques to solve problems in new or scarcely knwon enviroments in multidisciplinary contexts. 3. Ability to communicate to specialists and non-specialists, the conclusions using arguments about the instruments in which they are based. 4. Leaarning abilities to keep studying in an autonomous way. Competencias Generales: 1. Evaluation and comparison of alternative contributions to the important debates of the Social Sciences from an empirical point of view. 2. Evaluation of the arguments in scietific publications, with special focus on the consistency between theory and empirical results. 3. Give scientific arguments with clarity and precision. 4. Use quantitative techniques in research projects. 5. Desing of reserch projects for testing general hypothesis of interest. Competencias Específicas: 1. Searching, analysis and understanding of the empirical properties of data associated with social phenomena. 2. Mastery of quantitative instruments to implement them in research. Skills: 1. Mastering of quantitative methods to carry out research in Sociology, Political Science and Economic History. 2. Ability to carry out causal analysis. 3. Mastering of empirical analysis of microdata: households, companies or individuals using models and procedures appropriate for cross-sectional and panel data. 4. Ability to replicate results from scientific papers and to explain the details of the replications. 5. Carrying out empirical studies using the procedures in a critical and creative way.
Description of contents: programme
1. Introduction 1.1. Econometrics and Micro-Econometrics 1.2. Causality vs Correlation 1.3. Structure of the Data 2. A brief review of asymptotic theory 2.1. Convergence in probability and convergence in distribution 2.2. Conditional expectation 2.3. Limit theorems 3. Multiple Regression Models 3.1. Causal Relationships and ceteris paribus analysis 3.2. Experimental idea and the selection problem 3.3. Multicollinearity 3.4. Heteroskedasticity 3.5. Instrumental variables Estimation 3.5.1. Instrumental variables (IV) and Two-Stage Least Squares (2SLS) 3.5.2. Endogenity Test and Validity Test 3.5.3. IV Solutions to the Omitted Variables and Measurement Error 4. Estimating Average Treatment Effects 4.1. Introduction 4.2. A Counterfactual Setting and the Self-Selection Problem 4.3. Identification 4.4. Regression Adjustment 4.5. Differences-in-Differences 4.6. Propensity Score Methods 4.7. Matching Methods 4.8. Instrumental Variables Methods 4.9. Regression Discontinuity Designs 5. Introduction to Quantile regression 5.1. Fundamentals of quantile regressions 5.2. How does quantile regression work? 5.3. Interpreting quantile regression
Learning activities and methodology
The course is composed by theoretical lectures where both blackboard and audiovisual media is used to present some theoretical concepts. In addition, there will be practical problem sets and presentation of some papers to link theoretical findings and applications in some empirical studies. Course software will be R (
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • Angrist and Pischke. Mostly Harmless Econometrics. An Empiricist's Companion. Princeton University Press. 2008
  • Jeffrey M. Wooldridge. Econometric Analysis of Cross Section and Panel Data. The MIT Press. 2010
  • Morgan and Winship. Counterfactuals and causal inference. Methods and Principles for Social Research. Cambridge University Press. 2007
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Imbens y Rubin. Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press. 2015
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus and the academic weekly planning may change due academic events or other reasons.