90350 - Causal Inference And Program Evaluation

Learning outcomes

The course illustrates the most recent identification strategies for the quantitative assessment of causal effects using observational data by referring to micro-econometric applications. It will cover matching and difference-in-differences strategies, and quasi-experimental approaches to identification. At the end of the class, student will be able: - to critically understand the application of these tools in the recent empirical economic literature; - to apply these approaches to design his/her own program evaluation.

Course contents

The class is available only in English.

The course follows the Microeconometrics by A. Ichino 2026/2027, which represents a pre-requisite (as some topics illustrated in the Ichino's class will not be discussed in the Causal inference class)

The course covers empirical strategies for applied (mainly micro-economics) research questions. The main goal of the course is to provide an overview of the statistical tools for counterfactual analysis and a deeper discussion of a selection of these tools (recent advances in difference-in-differences; synthetic control methods; instrumetnal variables quantile regression and regression discontinuity design), from the theoretical and empirical point of view.

The course illustrates the identification strategies, estimation and other related issues (eg internal and external validity) that are relevant for the assessment of causal effects (or equivalently treatment effects) using observational data.

The Fundamentals of Causal Inference are covered in the class Microeconometrics by A. Ichino  thus, we won't review randomized trials as benchmark of non-experimental methods. Experimental methods are illustrated and discussed in detail in other courses at graduate and undergraduate level.

Fixed effects and random effects model are covered in the Microeconometrics class. We will touch on fixed effects models, because they are related to the Difference-in-Differences approach to identification and this is a pre-requisite to discuss Synthetic Control approach. The emphasis of the course will be on Synthetic control and recent advances in the literature on Difference-in-Differences, moving from a review of the standard 2 period,2 groups Difference-in-Differences set-up.

We touch on Instrumental Variable Strategies in linear regressionas they are pre-requisite to discuss instrumental variables in the context of quantile regression and a pre-requisite to fuzzy-Regression Discontinuity. Compared to the material previously covered on instrumental variables, we will discuss advanced issues related to applications of the IV strategy in the context of quantile regression.

Features of each particular econometric tool will be illustrated from both the theoretical and practical point of view, often through the discussion of empirical applications.

The emphasis will be on the practical implementation of each approach.

Topics

1. Fundamentals of Impact Evaluation [
   • The Fundamental Problem of Causal Inference
   • Potential Outcomes Framework
   • Basic Approaches to Identification:Randomized Trials
   • Basic Approaches to Identification:Selection on Observables (Propensity Score Matching)]

THIS PART WILL BE COVERED IN THE COURSE B2154 - MICROECONOMETRICS, TAUGHT BY PROF. ANDREA ICHINO IN THE WINTER TERM (NOV-DEC)

2. Quasi-Experiments: IV in quantile regression settings (approximately 9 hours classes during week 1 and 2)

THE IV IN THE CONTEXT OF LINEAR REGRESSION ARE COVERED IN THE COURSE B2154 - MICROECONOMETRICS, TAUGHT BY PROF. ANDREA ICHINO IN THE WINTER TERM (NOV-DEC)

3. Quasi-Experiments: Jumps (approximately 6 hours classes during week 2 and 3)

• Regression Discontinuity Design: Sharp and Fuzzy Designs, Identification, Estimation, Falsification Checks
• Multiple cutoffs, Multiple Running Variables (eg Geographic RDD): insights
• External Validity: Extrapolating Away from the Cutoff

3. Strategies exploting the structure of the data (approximately 15 hours, including practice sessionsclasses during week 3 and 4 and 5)

• Difference-in-Differences and recent advances on two-way fixed effects and difference-in-differences with heterogeneous treatment effects
• Non-linear difference-in-differences
• Synthetic Control Methods
  

 

 

Readings/Bibliography

Il corso è offerto solo in lingua inglese.

Lectures will be based on the following books and articles. We will rely on many empirical applications, referring to the recent literature. The list of papers with empirical applications will be distributed at the beginning of the class.

BOOKS

Mostly Harmless Econometrics, Angrist and Pischke

Mastering 'Metrics: The Path from Cause to Effect, Angrist and Pischke

Causal inference: the mixtape

Chapters 1-2 and 6, 8 (selected parts) from Koenker (2015) Quantile Regression

ARTICLES

BACKGROUND reading list

Holland, Paul W (1986) Statistics and Causal Inference, Journal of the American Statistical Association 81 (396): pp. 945-970, with discussion

Abadie, Cattaneo (2018) Econometric Methods for Program Evaluation Annual Review of Economics 10:465–503

IV reading list

Angrist, J., Imbens, G. and Rubin, D. (1996) Identification of Causal Effects Using Instrumental Variables, Journal of the American Statistical Association, 91 (434) pp. 444-455, with discussion

Angrist, J. (2004) Treatment Effect Heterogeneity in Theory and Practice, The Economic Journal, 114 (494) p.C52-C83

 

DIDs reading list

Muralidharan, Karthik, and Nishith Prakash. 2017. "Cycling to School: Increasing Secondary School Enrollment for Girls in India." American Economic Journal: Applied Economics, 9 (3): 321-50.

Athey and Imbens (2006) Identification and Estimation in Nonlinear Difference-in-Differences Models, Econometrica 74(2) pp.431-497

De Chaisemartin and D'Haultfoeuille (2022) Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey, Econometrics Journal.

Abadie, A. and 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, 105(490), pp. 493-505.

Abadie, Alberto (2021) "Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects." Journal of Economic Literature, 59 (2): 391-425.

Arkhangelsky, Dmitry, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager (2021) "Synthetic Difference-in-Differences." American Economic Review, 111 (12): 4088-4118.

RDD reading list

Hahn, J. and Todd, P. and Van der Klaauw, W. (2001) Identification and Estimation of Treatment Effects with a Regression Discontinuity Design, Econometrica 69 (1)

Research work by Cattaneo and co-authors https://sites.google.com/site/matiasdcattaneo/

QUANTILE REGRESSION reading list

 

Koenker & Hallock (2001) Quantile regression, Journal of Economic Perspectives 15(4), pp. 143-156

Chesher, A. (2003) Identification in Nonseparable Models, Econometrica, Vol. 71, pp. 1405-1441 and the 2001 WP version of the paper!

Chesher, A. (2005) Nonparametric Identification under discrete variation, Econometrica, Vol. 73 (5), pp. 1525-1550

Abadie, A. et al. (2002) Instrumental Variable Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings, Econometrica, Vol. 70 (1), pp. 91-117

Brunello et al. (2009) Changes in Compulsory Schooling, Education and the Distribution of Wages in Europe, Economic Journal 110 pp. 516-539

Chernozhucov, V. et al. (2005) An IV Model of Quantile Treatment
Effects, Econometrica, Vol. 73 (1), pp. 245-261

Chernozhucov, V. et al. (2006) Instrumental Quantile Regression Inference for Structural and Treatment Effect Models, Journal of Econometrics pp. 491-525

Ma et al. (2006) Quantile Regression Methods for Recursive Structural Equation Models, Journal of Econometrics, Vol. 134 (2), pp. 471-506

Teaching methods

Each topic will be covered in class and, whenever possible, in a practice session - eithe synchronous or a-synchronous using resources available on the web (e.g. recorded classes by leading scholars as for example M. Cattaneo or Pedro Sant'Anna available on line for free)

The lessons will cover the presentation of theoretical and applied topics related to the various econometric tools. The applications will be illustrated in the classroom and then resumed in practical lessons in the computer lab (or inviting students to bring their own pc to class) using STATA (available to students at the University of Bologna through a Campus licence).

All teaching materials will be distributed through the e-learning platform of the University of Bologna.

Research articles listed among the references can be downloaded from the web. You may use the search engine: http://acnp.unibo.it/cgi-ser/start/it/cnr/fp.html

Most books in the reference list are available at the University libraries. You may check availability through the search engine http://sol.unibo.it/SebinaOpac/Opac?sysb =

To address the need to offer the whole course online, to make students' experience more interactive, I will experimentally adopt innovative teaching tools (such as peer instruction; see link with reference) using adequate techical support during lectures relying for instance on free available software such as Pingo (https://pingo.coactum.de/). Students do not need to install the software ahead but need to have a device (mobile phone or laptop) who can access internet during the lectures in which this approach will be implemented.

In addition, peer education requires a great deal of investment from students as students have to read the textbook before coming to class.

We will also rely on PINGO [PINGO [https://pingo.coactum.de/]] - as tool to interactively test progress in competences

 

Assessment methods

Il corso è offerto solo in lingua inglese.

Written exam

Students will take a 1-hour written exam. The exam will include open questions that may refer to theory or case studies. In addition they will be given selected output (from STATA or from published papers) to comment on.

According to the indications of the council of the School of Economics and Management, the indications on the graduation of the grade are reported.

• <18: insufficient

• 18-23: sufficient

• 24-27: good

• 28-30: excellent

• 30 sum laude: excellent with praise/with honors

Use of artificial intelligence. AI can be a useful tool to assist individual study with in-depth analysis, summaries and self-assessment paths. In general, it can be a useful tool for refining coding skills also in view of the writing of theses of an empirical nature. Due to the limited duration of the written exam, as far as the final exam is concerned, during the face-to-face exam, the use of AI is not authorized. Substantial use will not be allowed for the performance of the test. A statement regarding the use of AI for exam preparation support activities will be required. At the same time a limited, declared and non-substantial use of AI is allowed for support activities (synthesis, reformulations), and also to work on assigned take-home exercises, in particular exercises requiring data analysis and coding skills. Substantial use is not permitted.

Active participation to class activities contributes to the final grade (0-7 points)

 

 

Teaching tools

Slides, teaching material , practice using STATA.

Self-evaluation on-line tests can be made available through the e-learning platform https://elearning-cds.unibo.it/ or the PINGO plaftorm

Lectures involve the presentation of theoretical and applied issues of the various econometric methods. Applications are discussed in class and replicated during the computer laboratory session using STATA.

Software STATA: available for students of the Department of Economics (CAMPUS license) and at the Computer Lab of the School of Economics and Management.

Students will also be invited to attend lectures or short courses and seminars offered online by leading scholars in the field.

Link ad altre eventuali informazioni

https://www.kuleuven.be/english/education/teaching-tips/activating-students/peer-instruction)

Office hours

See the website of Margherita Fort