Spring 2022 - ECON 335 D100
Introduction to Casual Inference and Policy Evaluation (3)
Class Number: 3956
Delivery Method: In Person
Course Times + Location:
Mo 2:30 PM – 4:20 PM
AQ 3154, Burnaby
Exam Times + Location:
Apr 23, 2022
8:30 AM – 11:30 AM
SSCC 9000, Burnaby
1 778 782-3795
Provides an introduction to statistical methods used to analyze causal questions and evaluate policies. Discusses various approaches to drawing causal inferences from observational data. Students who have taken ECON 480 first may not then take this course for further credit.
Economists and other social scientists often seek to measure the real-world effects of policy. More generally, we often want to assess the real-world effects of some potential “cause” on an “outcome.” For example, does a university degree increase future earnings? Does public health insurance make people healthier? Do environmental regulations reduce pollution? Do stricter capital requirements change bank lending behaviour?
This course will introduce you to the statistical and econometric methods that applied researchers use to answer causal questions like these. We will develop ideas in the potential outcomes framework and apply them to data using the R software package. Topics may include randomized experiments, regression discontinuity, matching, difference-in-differences, and instrumental variables. There will be regular graded assignments that will give you hands-on experience with data analysis in R. By the end of the course, you will learn how to critically evaluate statements about causal relationships, and apply a variety of methods to draw causal inferences of your own using R.
- Review of Statistical Methods
- The Potential Outcomes Framework
- Randomized Experiments
- Introduction to Regression
- Instrumental Variables
- Regression Discontinuity Designs
- Fixed Effects and Standard Errors
- Participation 10%
- Assignments 25%
- Midterm 30%
- Final Exam 35%
J. D. Angrist and J.-S. Pischke “Mastering ‘Metrics: The Path from Cause to Effect,” Princeton University Press (2014).
Wickham and G. Grolemund “R for Data Science: Import, Tidy, Transform, Visualize, and Model Data,” O'Reilly Media (2017). [available online free at https://r4ds.had.co.nz]
Department Undergraduate Notes:
Please note that, as per Policy T20.01, the course requirements (and grading scheme) outlined here are subject to change up until the end of the first week of classes.
Final exam schedules will be released during the second month of classes. If your course has a final exam, please ensure that you are available during the entire final exam period until you receive confirmation of your exam dates.Students requiring accommodations as a result of a disability must contact the Centre for Accessible Learning (CAL) at 778-782-3112 or email@example.com.
***NO TUTORIALS DURING THE FIRST WEEK OF CLASSES***
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
TEACHING AT SFU IN SPRING 2022
Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place. Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (firstname.lastname@example.org or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.