OECO5017: PG Online Session 2B, 2025

Units OECO5017 PG Online Session 2B 2025 [Online]

Unit outline_

OECO5017: Causal Effects in Practice

PG Online Session 2B, 2025 [Online] - Online Program

Overview

In this unit we unpack the interpretation of data analysis methods to appropriately measure treatment effects in observational data, gather a deep understanding of bias, distinguish correlations from causations, and critically answer questions of interest to business, policymakers, and researchers. Emphasis will be placed on interpretation of regression estimates, understanding the assumptions required to allow inferences to be causal, and learning a toolkit of methods for causal interpretation using observational data. Students will use statistical software to implement these methods and craft a comprehensive policy evaluation for the private or public sector.

Unit details and rules

Academic unit	Economics
Credit points	6
Prerequisites ?	ODAT5021
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Strong statistical knowledge and coding experience (e.g., successful completion of ODAT5011, ODAT5021 and ODAT5022)
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Marian Vidal-Fernandez, m.vidal-fdez@sydney.edu.au

Assessment

The census date for this unit availability is 17 October 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Out-of-class quiz	Online Quiz 1 Quiz	10%	Week 02 Due date: 07 Oct 2025 at 23:59	1 hour	AI allowed
Outcomes assessed:
Interactive oral	Project pitch Pitching the project	10%	Week 03 Due date: 15 Oct 2025 at 19:30	10 min	AI limited - refer to Canvas
Outcomes assessed:
In-person written or creative task	Peer reviews 5 peer reviews	5%	Week 03 Due date: 15 Oct 2025 at 19:30	250-word equivalent	AI limited - refer to Canvas
Outcomes assessed:
Out-of-class quiz	Online Quiz 2 Quiz	10%	Week 04 Due date: 21 Oct 2025 at 23:59	1 hour	AI allowed
Outcomes assessed:
Interactive oral	Project interactive presentation Project interactive presentation	15%	Week 06 Due date: 05 Nov 2025 at 19:30	15 min	AI limited - refer to Canvas
Outcomes assessed:
Out-of-class quiz	Online Quiz 3 Quiz	10%	Week 06 Due date: 04 Nov 2025 at 23:59	1 hour	AI allowed
Outcomes assessed:
Out-of-class quiz	Online Quiz 4 Quiz	10%	Week 07 Due date: 11 Nov 2025 at 23:59	1 hour	AI allowed
Outcomes assessed:
Research analysis	Written research project Final part of the scaffolded assignment	30%	Week 08 Due date: 09 Nov 2025 at 23:59	1500 words	AI allowed
Outcomes assessed:

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (Schedule 1).

As a general guide, a High Distinction indicates work of an exceptional standard, a Distinction a very high standard, a Credit a good standard, and a Pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Answering questions: The Power of Descriptive Statistics	Workshop (1.5 hr)
Week 02	Revisiting Regression	Workshop (1.5 hr)
Week 03	Randomised Control Trials	Workshop (1.5 hr)
Week 04	Instrumental Variables	Workshop (1.5 hr)
Week 05	Regression Discontinuity Design	Workshop (1.5 hr)
Week 06	Difference-in-Differences	Workshop (1.5 hr)
Week 07	Project presentations	Presentation (1.5 hr)

Attendance and class requirements

As per FASS guidelines

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Mastering Metrics by Josh Angrist

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Formulate causal effect questions to examine real-world problems from the perspective of individuals, organisations, and/or policymakers.
LO2. Effectively use and interpret descriptive data, and articulate its limitations when answering causal effect questions.
LO3. Critically evaluate the assumptions and limitations of empirical methods and arguments discussed in class.
LO4. Develop effective writing skills tailored to answering causal effect questions.
LO5. Effectively employ technology to gather information from written, oral, and digital sources.
LO6. Develop effective oral communication skills tailored to answering causal effect questions.
LO7. Develop the ability to efficiently and effectively manage, analyse, evaluate, and use fact-checked information.
LO8. Plan, write, and present a causal effect question along with a well-reasoned answer.
LO9. Participate effectively in workshop discussions.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered.

Additional information

School of Economics Student Portal

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links