ECON 400: From Correlation to Causation: Applied Causal Inference
Syllabus
Course Information
Semester: Spring 2026
Delivery: Online (Syncronous), MW 07:25 PM - 08:40 PM
Office Hours: MW, 03:00 PM - 04:00 PM (or by appointment)
Course Description
The purpose of this course is to discuss some econometric methods used in making causal inference. As an organizing principle, the course will focus on research design and modern econometric methods for evaluating cause and effect. Issues in cross-section and panel data, focus on problems such as endogeneity, heterogeneity, unobserved heterogeneity, treatment effects, etc. Panel data does often have an explicit time component; nonetheless, this class will not deal with time-series econometrics.
One way to think about the course is that it will provide you with a toolbox and working knowledge of cross-sectional and panel data methods for use in empirical research. The course will not go into great depth in regard to any particular applied econometric method, but will instead aim to provide you with enough knowledge about each one to know when, and when not, to use it in your empirical research.
Pre- or corequisites: ECON 201S, ECON 202S, BNAL 206, BNAL 306, and ECON 311, along with a declared major at the University or permission of the Dean’s Office.
Limitations
Time limitations impose certain restrictions on what we can accomplish in a one-semester course. For example, we will not cover all of the methods you might need or should know. We also will not cover each method in excruciating detail. Arguably, you could build an entire course (research agenda) around each method.
Textbooks and Other Materials
Required Software
As this course focuses on the learning and application of econometric techniques, a computer or laptop with R and RStudio installed is required. Students can obtain the latest version of R from r-project.org. Students can obtain a free version of R-studio from posit.co. While students can directly code in R, it is recommended that student use RStudio to facilitate interactions with the R language. Students will also install numerous open source packages throughout the course.
Course Learning Objectives
- Categorize settings in which different causal inference strategies may be applied.
- Evaluate the strengths and weakness of different causal inference strategies.
- Apply different causal inference methods to appropriate research settings.
- Interpret estimates generated by causal inference methods.
- Critique claims of causality in analyses using observational data without random assignment.
Course Schedule
Below is a schedule for course topics with corresponding assignments. See Grading Policy for due dates.
Grading Policy
The evaluation for this course consists of the following elements: homework assignments, imagination exercises, and a final project (in addition to checkpoints throughout the semester). The final grade is comprised of the following elements:
Grades will be determined by the sum of points earned, and then converted using this table:
| Letter | Minimum | Maximum |
|---|---|---|
| A | 467 | 500 |
| A- | 450 | 466 |
| B+ | 433 | 449 |
| B | 417 | 432 |
| B- | 400 | 416 |
| C+ | 383 | 399 |
| C | 367 | 382 |
| C- | 350 | 366 |
| D+ | 333 | 349 |
| D | 317 | 332 |
| D- | 300 | 316 |
| F | 0 | 299 |
Except for grades of “Incomplete”, all grades are considered final when reported by a faculty member at the end of a semester. A change in grade may only be requested when a calculation, clerical, administrative, or recording error is discovered in the original assignment of a course grade or when a decision is made by the faculty member to change the course grade because of the disputed academic evaluation procedures.
Grade changes necessitated by a calculation, administrative, or recording error must be reported within a period of six months from the time the grade is awarded. No grade may be changed as the result of a re-evaluation of a student’s work or the submission of supplemental work following the close of a semester.
Homework
The homework assignments will focus on applying each causal inference method covered in the module to a different data set in a different context from the material presented in the text. The final product must be an html document rendered by Quarto in RStudio. It must contain comments describing the code and appropriate variable names. It must generate regression output in table form and contain descriptions of the causal inference methods, data, and full interpretation of the results. Each assignment will come with an associated template.
Note: It is likely that students will have to venture out onto the internet by themselves to find solutions to homework questions.
Imagination Exercises
One of the class learning objectives is “Apply different causal inference methods to appropriate research settings.” There are many ways to apply these methods. One is to imagine how you might apply each method covered in class to a case that is salient to you. These exercises focus on that idea.
The imagination exercises require no data analysis or R coding. These exercises will get you thinking about research design, the methods covered in the course, and why they apply to specific situations, using an example that has meaning to you personally. Examples include papers read in other classes that did not use causal inference methods, economic news items of personal interest, questions related to your work or hobbies, or economic policy issues you find interesting. You must complete the imagination exercises using Quarto in RStudio and submit a rendered html document.
Final Project
The goal of the final project is to write an empirical paper using the tools covered in this course. The focus of the paper should be on the identification strategy and data analysis. See the Final Project’s page for more information.
It should be noted that, including the checkpoints, the final project is a large portion of your final grade. Therefore, it’s important to keep up with the checkpoints and be in communication with me about your project throughout the course. It should also noted that no checkpoint may be submitted without the checkpoint before it having been submitted.
Your project will be uploaded to, or embedded within, your ePortfolio by the end of the course.
Alternative Assignments
In this course, Project Checkpoints 3 through 7 offer a unique feature that you may consider “off-ramping” from the final project. At any point, you may choose not to submit a project checkpoint and instead complete an alternative assignment in its place. These alternatives will be worth the same amount of points as the checkpoints they replace.
Important Notes:
- Once you opt to complete an alternative assignment instead of a project checkpoint, you cannot return to the main project track. Choosing an alternative assignment effectively ends your participation in the project.
- Since each subsequent project checkpoint requires the previous one to have been completed, electing not to complete Checkpoint \(t\) results in a zero for Checkpoint \(t+1\) and beyond, including the final project. However, the alternative assignments will of course remain available.
- By taking an off-ramp, you commit to the alternative track for the remainder of the course, with a maximum attainable grade of B+ (due to earning a zero on the final project).
- Should a student submit both a checkpoint and its alternative, the alternative will take priority in terms of grading and the student will be considered off the project track.
This option is designed for students who may find the alternative assignments better align with their skills or goals. It also offers a form of “grade insurance” for those who prefer a more predictable path to success. If you anticipate challenges with meeting the demands of the project checkpoints, please consider the off-ramp as a viable option.
ePortfolio
In an effort to help students reflect on and synthesize their learning experiences, as well as demonstrate their skills to potential employers, certain courses taught by faculty in the Economics department will require the creation of, or addition to, an ePortfolio. Given the status of this course as the capstone of the Economics major, this course will contain an ePortfolio component.
Final projects will be submitted via Canvas and uploaded to each student’s ePortfolio. The extent to which students use their ePortfolio is ultimately up to them, but having their capstone project online and visible should differentiate them from competing job seekers. As a note, all material generated in this course will be portable .html files that can easily be uploaded to ePortfolios.
Online ePortfolio resources for ODU students can be found at odu.edu/asis/eportfolio.
Disclaimer: this course incorporates various online software and other technologies. Some technologies require you to either create an account on an external site or develop assignment content using them. The content, as well as your name/username or other personally identifying information may be publicly available as a result. While the purpose of these assignments is to engage with technology as a means for representing the content we are covering in class, please see me for an alternative activity if you object to potentially sharing your account, name, or other content you create in these technologies.
Incomplete Grades
A grade of “I” indicates assigned work yet to be completed in a given course, or absence from the final examination, and is assigned only upon instructor approval of a student request. The “I” grade may be awarded only in exceptional circumstances beyond the student’s control. The “I” grade becomes an “F” if not removed by the day grades are due for following term based on specific criteria: Incomplete, Withdraws and Z grades.
Course Policies
Communication
Students should feel welcome to contact me via email (acardazz@odu.edu) or drop by my office. I have an ‘open door’ policy for student questions and strongly encourage students to communicate with me. I respond to well-crafted emails within 48 business hours.
Students should take the time to craft complete, professional emails. The more information that you can provide about a question or problem, the more likely that my response will be helpful. Avoid non-professional language and practice communicating in the corporate workplace. Emails that are unprofessional will be returned with no action. There are many guides on how to compose a professional email which you can easily find online.
Attendance and Participation
There is no formal attendance policy for this course, but missing class will make it difficult to succeed. On days where the class is flipped, however, students are required to both present their imagination exercise and a published/working paper using the method we’re studying. If students are absent from class during these days, no points will be awarded for their imagination exercise (unless excused beforehand).
Late Assignments
All due dates are firm. Late submissions of any assignment will receive a score of zero unless discussed at least forty-eight hours prior to the deadline. Special circumstances that are communicated in advanced will be handled on a case by case basis.
Plagiarism
Plagiarism and turning in work that is not yours is grounds for being assigned a zero on an assignment, is a violation of the University Honor Code, and could result in failure in the course and/or academic action by the university.
Artificial Intelligence
You are currently enrolled in a course you can think of as a simultaneous introduction to both econometrics and the programming language R. You are enrolled in said course during a time in which artificial intelligence (AI) is booming. It is quite possible that AI will end up being the most transformative technology since the internet, and ignoring it would be foolish. As we get started in this course, I want to provide a few additional thoughts on AI and its use in this course.
As I am sure you understand by now, education is having to rapidly adjust to AI. This means that much of what previously worked, especially with regards to assessment, no longer does. At this point, the only path forward is to embrace the idea that AI will forever be part of the “toolkit,” much like how calculators, spell-check, and search engines are. Therefore, cautious AI use is allowed in this course. Reckless AI use, much like reckless use of other tools (e.g., copying and pasting text from Wikipedia and claiming it as your own), remains banned.
What constitutes appropriate AI use? Appropriate AI use is collaborative rather than substitutive. Using AI to help you understand why your code is not working is appropriate (collaborative), but having it write the code for you is not and may be considered reckless (substitutive). When determining the appropriateness of their AI use, students may find it helpful to ask themselves whether someone with no training, but access to an AI, could have produced the same thing. If the answer is yes, then the student has not added any value, and their AI use was substitutive. Students may also frame this question through the lens of employment and ask themselves whether someone would hire them for what they produced, or if this is instead something an AI could produce (for much a lower cost)? Companies are chomping at the bit to cut labor costs by replacing employees with AI; do something that makes this a difficult decision for them.
To encourage students to use AI collaboratively (or not at all), students abiding by this policy may be given the opportunity to resubmit homework. See the Revising Assignments section of the Syllabus for more details. On the other hand, students who are suspected of reckless AI use will be given a grade of zero for the specific questions unless they can prove otherwise (via a live, oral explanation of their answers recorded over Zoom, or a link to an AI chat).
Lastly, in the interest of transparency, detecting AI use is in fact not terribly difficult (albeit imperfect). Just like how individuals have particular ways of writing and speaking, they also have particular ways of coding. This can be the way they use spaces, capitalization, comments, etc. Each student in this course will develop their own specific way of writing/structuring code that looks and feels like their own. Moreover, when you learned to write sentences, you probably made simple mistakes like starting with “but” or “and,” or forgot to capitalize a proper noun, etc. As you are learning to code, you will make the programming version of these mistakes. Your use of space will be inconsistent, you will forget to use comments, and your code will sometimes be inefficient or verbose. All of these things are totally OK and part of the learning process. On the other hand, the code AI will generate will make it look like you have been coding for years. AI will use functions and libraries that do not appear in the course notes, it will provide perfect documentation, and adhere to obscure style guides. I have seen it happen time and time again, so it’s easy to spot.
Directions for using AI
To use AI in this course, students must provide a link to their chat. So long as AI use is documented and not simply asking for answers, using AI is approved (even encouraged).
Revising Assignments
Students may occasionally be invited to revise their answers to questions on certain assignments for half credit on what was initially marked as incorrect. Note that blank answers, what I deem to be “low effort” answers, or answers that have been flagged for reckless AI use will not be invited for revision. Requests from students to revise their work will be promptly declined. To be clear, the purpose of this policy is to reduce the relative benefits of reckless AI use or other forms of academic dishonesty. The hope is that the potential to revise high effort answers encourages students to submit high effort answers, even if they are not completely correct.
Course Disclaimer
The course schedule and activities are subject to change. Changes will be posted as Announcements in Canvas. All instructional materials and homework assignments can be found here.
University Policies
Code of Student Conduct and Academic Integrity
The Office of Student Conduct & Academic Integrity (OSCAI) oversees the administration of the student conduct system, as outlined in the Code of Student Conduct. Old Dominion University is committed to fostering an environment that is: safe and secure, inclusive, and conducive to academic integrity, student engagement, and student success. The University expects students and student organizations/groups to uphold and abide by standards included in the Code of Student Conduct. These standards are embodied within a set of core values that include personal and academic integrity, fairness, respect, community, and responsibility.
Honor Pledge
By attending Old Dominion University, you have accepted the responsibility to abide by the Honor Pledge:
I pledge to support the Honor System of Old Dominion University. I will refrain from any form of academic dishonesty or deception, such as cheating or plagiarism. I am aware that as a member of the academic community it is my responsibility to turn in all suspected violations of the Honor Code. I will report to a hearing if summoned.
Discrimination Policy
The purpose of this policy is to establish uniform guidelines to promote a work and education environment that is free from harassment and discrimination, as defined below, and to affirm the University’s commitment to foster an environment that emphasizes the dignity and worth of every member of the Old Dominion University community. The Discrimination Policy details the process to address complaints or reports of retaliation, as defined by this policy.
Diversity and Inclusion
The Division of Student Engagement & Enrollment Services values the uniqueness of our Monarch community. The word “engagement” reflects our commitment to embrace the differences in our cultural backgrounds, perceptions, beliefs, traditions, world views, socio-economic status, cognitive and physical abilities.
We will strive to serve as the pre-eminent model for engaging every student to achieve their own success. Our core values are fueled by our responsibility and actions toward community development and engagement, cultural competence and understanding, physical and mental wellness and inclusion for every member of ODU. We will embrace our greatest strength - the diverse composition of our student body and workforce. For more information regarding diversity and inclusion, please visit the Office of Intercultural Relations.
Educational Accessibility and Accommodations
Old Dominion University is committed to ensuring equal access to all qualified students with disabilities in accordance with the Americans with Disabilities Act. The Office of Educational Accessibility (OEA) is the campus office that works with students who have disabilities to provide and/or arrange reasonable accommodations.
The Accommodations for Students with Disabilities define the procedures used to accommodate student with disabilities. Students are encouraged to self-disclose disabilities that the Office of Educational Accessibility has verified by providing Accommodation Letters to their instructors early in the semester in order to start receiving accommodations. Accommodations will not be made until the Accommodation Letters are provided to instructors each semester
University Email Policy
With the increasing reliance and acceptance of electronic communication, email is considered an official means for University communication. Old Dominion University provides each student an email account for the purposes of teaching and learning, research, administration, and service. It is the responsibility of every eligible student to activate MIDAS, the Monarch Identification and Authorization System, to obtain email access. It is important that all students are aware of the expectations associated with email use as outlined in the Student Email Standard. The email account provided by the University is considered to be an official point of contact for correspondence. Students are expected to check their official e-mail account on a frequent and consistent basis in order to stay current with University communications. Mail sent to the ODU email address may include notification of University-related actions, including academic, financial, and disciplinary actions. For more information about student email, please visit Student Computing.
Withdrawal
A syllabus constitutes an agreement between the student and the course instructor about course requirements. Participation in this course indicates your acceptance of its teaching focus, requirements, and policies. Please review the syllabus and the course requirements as soon as possible. If you believe that the nature of this course does not meet your interests, needs or expectations, if you are not prepared for the amount of work involved – or if you anticipate assignment deadlines or abiding by the course policies will constitute an unacceptable hardship for you – you should drop the course by the drop/add deadline, which is listed in the ODU Schedule of Classes. For more information, please visit the Office of the University Registrar.
Privacy of Student Information
Old Dominion University recognizes its duty to uphold the public’s trust and confidence, not only in following laws and regulations, but in following high standards of ethical behavior. Members of the Old Dominion University community are responsible for maintaining the highest ethical standards and principles of integrity. The Code of Ethics is a set of values-based statements that demonstrate the University’s commitment to this goal. The Privacy of Student Information details Family Educational Rights & Privacy Act (FERPA), along with other information regarding privacy.
Other Academic Policies
Please see the following link for other academic policies at the university level: https://catalog.odu.edu/undergraduate/policies/academic-policies/