THE UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL
SCHOOL OF SOCIAL WORK
COURSE NUMBER: SOWO 919
COURSE TITLE, SEMESTER AND YEAR: Advanced Topics in Causal Inference: Propensity Score
and Related Models
Fall semester, 2011
INSTRUCTOR: Shenyang Guo, Ph.D.
School of Social Work
Address:
Room 524j, Tate Turner Kuralt Building
CB #3550,
Chapel Hill, NC 27599-3550
Phone: (919) 843-2455
Fax: (919) 962-1486
Email: sguo@email.unc.edu
CLASS MEETINGS: 9:00-11:50, Fridays
OFFICE HOURS: 8:30-10:30, Tuesdays (Room 524j TTK)
COURSE DESCRIPTION:
This course focuses on advanced topics in causal inference by reviewing four recent methods
developed for observational studies and evaluation of quasi-experimental programs.
COURSE OBJECTIVES:
At the completion of the course, students will be able to:
1. Understand challenges posted by evaluation of quasi-experimental or observational data,
contexts under which randomized experiments are infeasible, unethical, and expensive,
and the importance of taking remedial strategies within such contexts;
2. Understand differences, debates, and similarities between statistical and econometric
traditions in developing analytical strategies to overcome challenges posted by quasiexperimental and observational data;
3. Have a solid understanding of the Neyman-Rubin’s counterfactual framework and two
fundamental assumptions: the strongly ignorable treatment assignment, and the stable
unit treatment value. Understand Heckman’s critiques to the counterfactual framework
and main features of the Heckman’s scientific model of causality;
4. Understand the main features of Heckman’s sample selection and related models, and
know how to implement the analysis with Stata;
5. Understand the main features of propensity-score greedy matching and related models,
and know how to implement the analysis with Stata;
6. Understand the main features of propensity-score optimal matching and related models,
and know how to implement the analysis with Stata and R;
7. Understand the main features of matching estimators, and know how to implement the
analysis with Stata;
8. Understand the main features of kernel-based matching and related models, and know
how to implement the analysis with Stata;
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9. Understand the main features of Rosenbaum’s sensitivity analysis to evaluate potential
bias due to hidden selection, and know how to implement the analysis with Stata;
10. Know how to read, evaluate, and criticize evaluation studies.
EXPANDED DESCRIPTION:
This course will review four closely related but technically distinct propensity score
models developed for intervention research when randomized clinical trials are infeasible or
unethical: (a) Heckman’s sample selection model (Heckman, 1976, 1978, 1979) and its revised
version estimating treatment effects (Maddala, 1983); (b) propensity score matching
(Rosenbaum and Rubin, 1983) and related models; (c) matching estimators (Abadie, Drukker,
Herr, & Imbens, 2004); and (d) propensity score analysis with nonparametric regression
(Heckman, Ichmura, & Todd, 1997, 1998). Learning of these models will be guided by two
conceptual frameworks: the Neyman-Rubin counterfactual framework and the Heckman
scientific model of causality. The course also covers Rosenbaum’s approaches of sensitivity
analysis to discern bias produced by hidden selections.
PRE-REQUIREMENT:
Students are assumed to be familiar with descriptive and inferential statistics. They
should have statistical and statistical software background at least equivalent to that provided by
SOWO 919 “Applied Regression Analysis and Generalized Linear Models”. Students without
such prerequisites should contact the instructor to determine their eligibility to take the course.
STATISTICAL SOFTWARE PACKAGE:
This course will use Stata as the main software package.
REQUIRED TEXTS/READINGS:
Guo, S. & Fraser, W.M. (2010). Propensity Score Analysis: Statistical Methods and Applications.
Thousand Oaks, CA: Sage Publications.
All required journal articles are available on E-journals and the course Blackboard site.
RECOMMENDED TEXTBOOKS:
Rosenbaum, P. R. (2010). Design of Observational Studies. New York: Springer.
Morgan, S.L, & Winship, C. (2007). Counterfactuals and Causal Inference: Methods and
Principles for Social Research. New York: Cambridge University Press.
Assignments
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Assignment 6
Final Exam (take home)
Grade Percentage
10%
10%
10%
10%
10%
10%
40%
GRADING SYSTEM
The standard of School of Social Work’s interpretation of grades and numerical scores will be
used.
sowo 919: Advanced Topics in Causal Inference: Propensity Score and Related Models
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H = 94-100
P = 80-93
L = 70-79
F = 69 and below
POLICY ON CLASS ATTENDANCE
Class attendance is an important element of class evaluation, and you are expected to attend all
scheduled sessions. Each class session will cover a great deal of materials, and you will fall
behind the course when you miss even one class session. It’s student’s responsibility to inform
the instructor via email in advance for missing a class session. You are expected not to miss
more than two sessions for the whole semester. Starting from the second missing, your course
grade will be reduced by 10% for each session missed.
POLICY ON INCOMPLETE AND LATE ASSIGNMENTS
Assignments are to be turned in to the professor by 5pm of the due date noted in the course
outline. Extensions may be granted by the professor given advance notice of at least 24 hours.
Late assignments (not turned in by 5pm on the due date) will be reduced 10 percent for each day
late (including weekend days). A grade of incomplete will only be given under extenuating
circumstances and in accordance with University policy.
POLICY ON ACADEMIC DISHONESTY
Students are expected to follow the UNC Honor Code. Please include the honor code statement
along with your signature on all assignments:
“I have neither given nor received unauthorized aid on this assignment.”
Please refer to the APA Style Guide, the SSW Manual, and the SSW Writing Guide for
information on attribution of quotes, plagiarism and appropriate use of assistance in preparing
assignments.
If reason exists to believe that academic dishonesty has occurred, a referral will be made to the
Office of the Student Attorney General for investigation and further action as required.
POLICY ON ACCOMMODATIONS FOR STUDENTS WITH DISABILITIES
Students with disabilities which affect their participation in the course may notify the instructor
if they wish to have special accommodations in instructional format, examination format, etc.,
considered.
COURSE OUTLINE (TOPICS, READINGS, AND ASSIGNMENTS):
08-26-11
Session 1: Introduction and Course Overview
1. Observational studies and challenges
2. History and development
3. Fisher’s randomized experiment
4. Why and when propensity score analysis is needed?
5. Course overview
Readings:
Guo & Fraser, chapter 1.
09-02-11
Session 2: Counterfactual Framework
1. The Neyman-Rubin’s counterfactual framework
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2. The assumption about strongly ignorable treatment assignment
3. The stable unite treatment value assumption
4. Types of treatment effects
Readings:
Guo & Fraser, chapter 2 (pp.21-50).
Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and
nonrandomized studies. Journal of Educational Psychology, 66, 688-701.
Holland, P. (1986). Statistics and causal inference (with discussion). Journal of the
American Statistical Association, 81, 945-970.
09-09-11
Session 3: Intervention Research and Simple Methods for Data Balancing
1. Guest speaker Dr. Mark Fraser: Intervention research and observational studies
2. Summary of the two traditions in developing nonexperimental models
3. Review of three simple methods for data balancing
4. Key issues regarding the OLS regression
Readings:
Fraser, M.W., Richman, J.M., Galinsky, M.J., & Day, S.H. (2009). Intervention
research. New York, NY: Oxford University Press. Chapters 1, 2, & 7.
Guo & Fraser, chapter 3.
09-16-11
Session 4: Sample Selection and Related Models (Part 1)
1. Truncation, censoring, and incidental truncation
2. Key features of Heckman’s sample selection model
3. Treatment effect model
4. Illustrations
Readings:
Guo & Fraser, chapter 4: 4.1-4.2.
Heckman, J. J. (1978). Dummy endogenous variables in a simultaneous equations
system. Econometrica, 46, 931-960.
Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica,
47, 153-161.
09-23-11
1.
2.
3.
4.
Readings:
Session 5: Sample Selection and Related Models (Part 2) and Instrumental
Variables Estimator
The instrumental variables estimator
Review basics of running Stata
Overview of Stata programs analyzing sample selection
Stata lab: running treatreg
Guo & Fraser, chapter 4: 4.3-4.6.
Journal of the American Statistical Association (1996), Vol. 91, Number 434
Assignment 1 out (Due: 9-30-11): Exercises of running Heckit treatment effect model.
09-30-11
1.
2.
3.
4.
5.
Session 6: Propensity Score Matching (Part 1)
Overview of propensity score matching
The Rosenbaum and Rubin’s model (1983)
Strategies to seek optimal propensity scores
Greedy matching
Computer lab: running greedy matching with psmatch2
sowo 919: Advanced Topics in Causal Inference: Propensity Score and Related Models
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Readings:
Guo & Fraser, chapter 5: 5.1-5.3, 5.4.1, 5.5.1, 5.5.2.
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in
observational studies for causal effects. Biometrika, 70, 41-55.
D’Agostino, R. B., Jr. (1998). Tutorial in biostatistics: Propensity score methods for
bias reduction in the comparison of a treatment to a non-randomized control
group. Statistics in Medicine, 17, 2265-2281.
Assignment 1 due
Assignment 2 out (Due: 10-07-11): Exercise of running propensity score greedy matching with a
multivariate post-matching analysis and a subclassification analysis.
10-07-11
Session 7: Propensity Score Matching (Part 2)
1. Optimal matching
2. Post-optimal-matching analysis
3. Propensity score weighting
Readings:
Guo & Fraser, chapter 5: 5.4.2, 5.5.3-5.5.6, 5.6-5.7.
Haviland, A., Nagin, D. S., & Rosenbaum, P. R. (2007). Combining propensity score
matching and group-based trajectory analysis in an observational study.
Psychological Methods, 12, 247-267.
Assignment 2 due
10-14-11
Session 8: Propensity Score Matching (Part 3)
1. Introduction to R
2. Running optimal matching with R
2. Conducting post-matching analysis
Readings:
Guo & Fraser, chapter 5: 5.8-5.10.
Assignment 3 out (Due: 10-28-11): Exercise of running optimal propensity score matching (pair,
variable, and full matching) with a post-matching regression adjustment and a
Hodges-Lehmann aligned rank test.
10-21-11
No class: happy fall break!
10-28-11
Session 9: Matching Estimators
1. Simple matching estimator
2. Bias-corrected matching estimator
3. Variance estimator allowing for heterosckedasticity
4. Efficacy subset analysis
5. Stata lab: running nnmatch
Readings:
Guo & Fraser, chapter 6.
Abadie, A., Drukker, D., Herr, J. L., & Imbens, G. W. (2004). Implementing
matching estimators for average treatment effects in Stata. The Stata Journal, 4,
290-311.
Assignment 3 due
Assignment 4 out (Due: 11-04-11): Exercise of running matching estimators.
11-04-11
Session 10: Propensity Score Analysis with Nonparametric Regression
1. The kernel-based matching estimator
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2. Review of the basic concepts of lowess
3. Stata lab: running kernel-based matching with psmatch2
Readings:
Guo & Fraser, chapter 7.
Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric
evaluation estimator: Evidence from evaluating a job training programme.
Review of Economic Studies, 64, 605-654.
Heckman, J. J., Ichimura, H., & Todd, P. E. (1998). Matching as an econometric
evaluation estimator. Review of Economic Studies, 65, 261-294.
Abadie, A., Imbens, G.W. (2008). On the failure of the bootstrap for matching
estimators. Econometrica, 76(6):1537–1557.
Assignment 4 due
Assignment 5 out (Due: 12-02-11): Classroom presentation. Students will be divided into small
groups. Each group will present study findings on the following questions: (1)
What key challenges researchers may encounter in observational studies? (2)
What do you learn from the debates between econometricians and statisticians?
(3) What core issues may be summarized from the literature with regard to
selection bias? (4) What do you learn from the critiques to the nonexperimental
approaches?
11-11-11
Session 11: Selection bias and Sensitivity Analysis (Part 1)
1. Overview of selection bias
2. Rosenbaum’s sensitivity analysis
Readings:
Guo & Fraser, chapter 8.
Rosenbaum, P.R. (2005). Sensitivity analysis in observational studies. In B.S. Everitt
and D. C. Howell (Eds.) Encyclopedia of Statistics in Behavioral Science
(pp.1809-1814). New York: John Wiley & Sons, Ltd.
11-18-11
Session 12: Selection bias and Sensitivity Analysis (Part 2)
1. Stata lab: running sensitivity analysis with rbounds
2. Critical review of evaluation studies
3. Criticism toward nonexperimental approaches
Readings:
Guo & Fraser, chapter 9: 9.1-9.2.
Michalopoulos, C., Bloom, H. S., & Hill, C. J. (2004). Can propensity-score methods
match the findings from a random assignment evaluation of mandatory welfareto-work programs? The Review of Economics and Statistics, 86, 156-179.
Agodini, R., & Dynarski, M. (2004). Are experiments the only option? A look at
dropout prevention programs. The Review of Economics and Statistics, 86, 180194.
Assignment 6 out (Due: 12-02-11): Exercise of running kernel-based matching and differencein-differences analysis; and exercise of running Rosenbaum’s sensitivity
analysis using Wilcoxon’s signed rank test.
11-25-11
No class: happy Thanksgiving!
12-02-11
Session 13: Student Presentation and Heckman’s Model of Causality
1. Student presentation
2. Overview of Heckman’s model of causality
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3. Debates between econometricians and statisticians
4. Other advances in modeling causality and future development
Readings:
Guo & Fraser, chapter 2 (pp.50-54).
Guo & Fraser, chapter 9: 9.3-9.4.
Heckman, J. J. (2005). The scientific model of causality. Sociological Methodology,
35, 1-97.
Sobel, M. E. (2005). Discussion: “The scientific model of causality.” Sociological
Methodology, 35, 99–133.
Assignment 5 due
Assignment 6 due
Final exam out (Due: 12-16-11): In this take-home exam, you are required to write a paper
comparing at least two propensity score methods. The paper should include: (1)
a brief introduction to describe research questions and hypotheses; (2) a method
section to describe correction methods being compared; (3) findings, and (4)
discussion. The paper should meet the requirements and standards for its
publication in a peer-reviewed journal.
12-16-11:
Final exam due
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