Causal Analysis with Observational Data

Instructor: Michael Grätz

Modality: In presence

Week 2: 19-23 August 2024

Workshop Contents and Objectives

Does smoking cause bad health? Does income inequality increase political extremism? Do schools increase inequality? Many questions of interest to social scientists are causal. This course provides an introduction to modern methods of causal inference using observational data. Building on the potential outcomes framework to causality the course discusses natural experiments, instrumental variables, difference-in-differences (DID), different types of fixed effects models, and regression discontinuity designs (RDD). All these methods allow researchers to control for unobserved variables and therefore to identify causal effects using observational data. The course also provides an introduction to Directed Acyclic Graphs (DAG), which allows us to graphically depict causal relationships.

Workshop design

The course provides both a sound understanding of each method as well as practical exercises to implement these methods using R and Stata.
There will also be plenty of time to discuss research projects and ideas related to the methods of the course by the participants. Participants are very much encouraged to apply the methods taught in the course to their own research questions.

The workshop has three aims:

• To introduce you to each method.
• To learn how to implement each method in R and/ or Stata and how to interpret its results for a research paper.
• To discuss how to justify and criticize the use of each method in the analysis of your own research and published journal articles of other researchers.

Each of these three aims is covered each day by one of three sessions. We start each day in the morning (8:30–10:00) with a lecture about the methodological background to each method. After a short break, we continue with the implementation of each method in R and/ or Stata and the interpretation of the output (10:30–12:00). After lunch, we discuss how to justify and to criticize the use of each method (13:00–15:30/16:00). This is done by the analysis of both published articles and your own research. You’re very welcome to think about how to implement each method within your own research in this part of the course!

Detailed lecture plan (daily schedule)

Day 1.
The counterfactual framework of causality (including the potential outcomes framework) and Directed Acyclic Graphs (DAGs).

Day 2.
Sibling and twin fixed effects.

Day 3.
Difference-in-differences.

Day 4.
Instrumental variable estimation.

Day 5.
The regression discontinuity design.

On the first day the idea is that you present your own research (a paper, a research question from your thesis, etc.) in the form of a DAG to the other participants in the afternoon. Do not worry about it now, you’ll have plenty of time to think about how to do this on the first day of the course.

Class materials

The course is accompanied by the eLearning platform iCorsi. I will upload all material there. You will receive the sign up details from Agata Lambrechts or Eleonora Vicari from USI in late June. Please enroll to the eLearning platform, as you need it to access the course’s material.
If you have any questions about the course before we start, please drop me an e-mail.

Prerequisites

Some elementary knowledge of regression analysis, in particular linear regression, will be necessary to be able to fully follow the content of the course. Statistical analyses will be conducted with R and Stata. A general knowledge of one of these languages will be necessary to implement the practical exercises, as there won’t be the time to learn basic commands. Participants can conduct all exercises in R or Stata, according to their own preferences.

Recommended readings or preliminary material

There are two reading lists:
(a) References about the methods. Here you can read up if you have missed something in the lecture. This reading list is not compulsory.
(b) A list of compulsory reading. These are the examples for each method. You have to read one article every day after lunch. The readings will be distributed among the participants of the course and be discussed in four groups

If you want to read one (and only one) book, I strongly recommend Firebaugh’s Seven Rules for Social Research. In my view, this book is an excellent introduction into how to do research (both descriptively and causally). Of course, you are welcome to read more! The classic and still best but very dense textbook for the methods discussed on days 3 to 5 is Angrist and Pischke (2009). The other books are maybe easier to read but are also lengthier. Morgan and Winship (2014) introduced DAGs into sociology but it is also a bit lengthy (the first edition was much shorter than the second one).
For a short introduction to the topics of the summer school course, you can also read Richard Breen’s contribution to the recent Handbook of Sociological Science. This chapter is freely available open access here: https://www.elgaronline.com/view/book/9781789909432/book-part-9781789909432-24.xml.

A. REFERENCES ABOUT THE METHODS (* = recommended)

Day 1: The counterfactual approach to causality and Directed Acyclic Graphs (DAGs)
* Angrist, Joshua, and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics. Princeton University Press. Chapters 1 to 3. Pages 3–110.
Cinelli, Carlos, Andrew Fourney, and Judea Pearl. 2022. “A Crash Course in Good and Bad Controls.” Sociological Methods and Research, DOI: 10.1177/00491241221099552.
Cunningham, Scott. 2021. Causal Inference. Yale University Press. Chapters 1 to 4. Pages 1–174.
* Elwert, Felix. 2013. Graphical causal models. Pp. 245-273 in Handbook of Causal Analysis for Social Research, edited by Stephen L. Morgan. Springer.
* Elwert, Felix, and Christopher Winship. 2014. “Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable. “ Annual Review of Sociology 40:31–53.
* Firebaugh, Glenn 2008. Seven Rules for Social Research. Princeton, NJ: Princeton University Press.
Holland, Paul W. 1986. Statistics and causal inference. Journal of the American Statistical Association, 81, 945–960.
Morgan, Stephen L., and Christopher Winship. 2014. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Second Edition. Cambridge: Cambridge University Press. Chapters 1 to 7. Pages 3–262.
Pearl, J. 2009. Causality. Cambridge University Press: Cambridge.
Pearl, J., and D. Mackenzie. 2019. The Book of Why: The New Science of Cause and Effect. New York: Basic Books.

Day 2: Sibling and twin fixed effects models
Allison, Paul D. 2009. Fixed Effects Regression Models. London: Sage.
* Firebaugh, Glenn 2008. Seven Rules for Social Research. Princeton, NJ: Princeton University Press. Chapter 5. Pages 140–146.
Brüderl, Josef, and Volker Ludwig. 2015. “Fixed-Effects Panel Regression.” Pp. 327–57 in The SAGE Handbook of Regression Analysis and Causal Inference, edited by H. Best and C. Wolf. London: Sage.

Day 3: Difference-in-differences (DiD)
* Angrist, Joshua, and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics. Princeton University Press. Chapter 5. Pages 227–243.
Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2004. “How Much Should We Trust Differences-in-Differences Estimates?” Quarterly Journal of Economics 119:249–75.
Cunningham, Scott. 2021. Causal Inference. Yale University Press. Chapter 9. Pages 406–510.
Huntington-Klein, Nick. 2022. The Effect: An Introduction to Research Design and Causality. Abingdon: CRC Press. Chapter 18. Pages 435–467.
Lechner, Michael 2011. “The Estimation of Causal Effects by Difference-in-Difference Methods.” Foundations and Trends in Econometrics 4:165–224.

Day 4: Instrumental variables (IV)
Angrist, Joshua, Guido Imbens, and Donald Rubin. 1996. “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association 91:444–55.
Angrist, Joshua, and Alan Krueger. 2021. “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments.” Journal of Economics Perspectives 14:69–85.
* Angrist, Joshua, and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics. Princeton University Press. Chapter 4. Pages 113–218.
Cunningham, Scott. 2021. Causal Inference. Yale University Press. Chapter 7. Pages 315–385.
Huntington-Klein, Nick. 2022. The Effect: An Introduction to Research Design and Causality. Abingdon: CRC Press. Chapter 19. Pages 469–503.
Morgan, Stephen L., and Christopher Winship. 2014. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Second Edition. Cambridge: Cambridge University Press. Chapter 9. Pages 291–324.

Day 5: Regression discontinuity design (RDD)
* Angrist, Joshua, and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics. Princeton University Press. Chapter 6. Pages 251–267.
Cunningham, Scott. 2021. Causal Inference. Yale University Press. Chapter 6. Pages 241–314.
Huntington-Klein, Nick. 2022. The Effect: An Introduction to Research Design and Causality. Abingdon: CRC Press. Chapter 20. Pages 505–554.
Lee, David S., and Thomas Lemieux. 2010. “Regression Discontinuity Design in Economics.” Journal of Economic Literature 48:281–355.

B. EXAMPLES OF PUBLISHED RESEARCH (ONE OUT OF FOUR ARTICLES EACH DAY IS COMPULSORY TO READ)

Day 1: The counterfactual approach to causality and DAGs (no examples on this day to have more time to discuss your research projects)

Day 2: Sibling and twin fixed effects models
Duncan, Greg J., W. Jean Yeung, Jeanne Brooks-Gunn, and Judith R. Smith. 1998. “How Much Does Childhood Poverty affect the Life Chances of Children?” American Sociological Review 63:406–23. DOI: 10.2307/2657556.
Grätz, Michael, and Florencia Torche. 2016. “Compensation or Reinforcement? The Stratification of Parental Responses to Children’s Early Ability.” Demography 53:1883–1904. DOI: 10.1007/s13524-016-0527-1. Labussière, Marie. 2023. “Timing of Citizenship Acquisition and Immigrants’ Children Educational Outcomes: A Family Fixed-Effects Approach.” European Sociological Review, DOI: 10.1093/esr/jcad027.
Laidley, Thomas, Benjamin Domingue, Piyapat Sinsub, Kathleen M. Harris, and Dalton Conley. 2019. “New Evidence of Skin Color Bias and Health Outcomes Using Sibling Difference Models: A Research Note.” Demography 56:75–62. DOI: 10.1007/s13524-018-0756-6.

Day 3: DiD Aksoy, Ozan, and Diego Gambetta. 2022. “Commitment through Sacrifice: How Longer Ramadan Fasting Strengthens Religiosity and Political Islam.” American Sociological Review 87: 555–83. DOI: 10.1177/00031224221101204. Frey, Arun, and David S. Kirk. 2021. “The Impact of Mass Shootings on Attitudes toward Gun Restrictions.” Socius, DOI: 10.1177/23780231211054636. Grätz, Michael. 2023. “Does Schooling Affect Socioeconomic Inequalities in Educational Attainment?” Sociological Science, DOI: 10.15195/v10.a31. Torche, Florencia. 2011. “The Effect of Maternal Stress on Birth Outcomes: Exploiting a Natural Experiment.” Demography 48:1473–91. DOI: 10.1007/s13524-011-0054-z.

Day 4: IV
Bonsang, Eric, and Vegard Skirbekk. 2022. “Does Childbearing Affect Cognitive Health in Later Life? Evidence from an Instrumental Variable Approach.” Demography 59:975–994. DOI: 10.1215/00703370-9930490. Grätz, Michael. 2023. “The Effects of Female Education on Child Education: A Prospective Analysis.” European Societies, DOI: 10.1080/14616696.2023.2275591. Kirk, David. 2009. “A Natural Experiment on Residential Change and Recidivism: Lessons from Hurricane Katrina.” American Sociological Review 74: 484–505. Laidley, Thomas, and Dalton Conley. 2018. “The Effects of Active and Passive Leisure on Cognition in Children: Evidence from Exogenous Variation in Weather.” Social Forces 97:129–56. DOI: 10.1093/sf/soy020.

Day 5: RDD Aksoy, Ozan, and Francesco C. Billari. 2018. “Political Islam, Marriage, and Fertility: Evidence from a Natural Experiment.” American Journal of Sociology, 123, 1296–1340. DOI: 10.1086/696193. Bernardi, Fabrizio. 2014. “Compensatory Advantage as a Mechanism of Educational Inequality: A Regression Discontinuity Based on Month of Birth.” Sociology of Education 87:74–88. DOI: 10.1177/0038040714524258. Grätz, Michael, and Marieke Heers. 2023. “Tracking in Context: Variation in the Effects of Reforms in the Age at Tracking on Educational Mobility.” SocArXiv, DOI:10.31235/osf.io/f5zug. Hainmueller Jens, Dominik Hangartner, and Dalston Ward. 2019. “The Effect of Citizenship on the Long-Term Earnings of Marginalized Immigrants: Quasi-Experimental Evidence from Switzerland.” Science Advances, DOI: 10.1126/sciadv.aay1610.