Microeconometrics
 Enseignant(s):
 Titre en français: Microéconométrie
 Cours donné en: anglais
 Crédits ECTS:
 Horaire: Semestre de printemps 20192020, 4.0h. de cours (moyenne hebdomadaire)
 séances
 Formation concernée: Maîtrise universitaire ès Sciences en économie politique
ObjectifsThis course discusses microeconometric methods for causal inference, i.e. to assess the causal impact of a specific explanatory variable (also referred to as "treatment" or "policy intervention") on an outcome (or "dependent variable") of interest. This may, for instance, concern the effectiveness of public policies (e.g. training programs for unemployed, income support for poor families, public childcare,…), corporate policies (marketing campaigns, educational programs for employees,…), health interventions (new medical treatments…), among many other examples. This course reviews and extends methods for causal inference/policy evaluation partly covered in the course "Econometrics", including matching, inverse probability weighting, doubly robust estimation, instrumental variables, difference in differences, changes in changes, and regression discontinuity and kink designs. It also provides an introduction to machine learning and its application to causal inference in big data contexts. Finally, it discusses the evaluation of mechanisms through which a treatment may affect an outcome (e.g. training may affect mental health via finding employment), known as causal mediation analysis. ContenusThe main topics covered in the course are the following:  The definition of causal effects (review of the “potential outcome” notation)
RéférencesBasic reading  briefly covers most topics of the course: Huber (2019): An introduction to flexible methods for policy evaluation Further reading  these papers cover the topics of the course in more detail (and even exceed the content of the course): Imbens and Wooldridge (2009): Recent developments in the Econometrics of Program Evaluation Lee and Lemieux (2010): Regression discontinuity designs in economics Lechner (2011): The Estimation of Causal Effects by DifferenceinDifference Methods Card, Lee, Pei, and Weber (2016): Regression kink design  Theory and practice Chernozhukov et al. (2018): Double/debiased machine learning for treatment and structural parameters Huber (2019): A review of causal mediation analysis for assessing direct and indirect effects Huber and Wüthrich (2019): Local average and quantile treatment effects under endogeneity  a review Further reading for an introduction to machine learning (exceeds the content of the course): PrérequisConnaissances solides en économétrie de base. Evaluation1ère tentative
Rattrapage

[» page précédente] [» liste des cours]