Outline

Overview

This unit covers modern statistical methods that are now available for assessing the causal effect of a treatment or exposure from a randomised or observational study. The unit begins by explaining the fundamental concept of counterfactual or potential outcomes and introduces causal diagrams (or directed acyclic graphs) to visually identify confounding, selection and other biases that prevent unbiased estimation of causal effects. Key issues in defining causal effects that are able to be estimated in a range of contexts are presented using the concept of the 'target trial' to clarify exactly what the analysis seeks to estimate. A range of statistical methods for analysing data to produce estimates of causal effects are then introduced. Propensity score and related methods for estimating the causal effect of a single time point exposure are presented, together with extensions to longitudinal data with multiple exposure measurements, and methods to assess whether the effect of an exposure on an outcome is mediated by one or more intermediate variables.. Comparisons will be made with 'conventional' statistical methods. Emphasis will be placed on interpretation of results and understanding the assumptions required to allow inferences to be called 'causal'. Stata and R software will be used to apply the methods to real datasets.

Unit details and rules

Unit code	BSTA5017
Academic unit	Public Health
Credit points	6
Prohibitions ?	None
Prerequisites ?	(PUBH5010 or BSTA5011 or CEPI5100) and (BSTA5210 or BSTA5007)
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Erin Cvejic, erin.cvejic@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Module 1 and 2 Exercises Recorded presentation based on Module 1 and Module 2 exercises	15%	- Due date: 22 Aug 2022 at 23:59	10 minutes (recorded)
Outcomes assessed:
Assignment	Module 3 Exercises Solutions to selected practical exercises	10%	- Due date: 12 Sep 2022 at 23:59	3-5 pages
Outcomes assessed:
Assignment	Major Assignment 1 Major assignment covering Modules 1 to 3	25%	- Due date: 26 Sep 2022 at 23:59	8-10 pages
Outcomes assessed:
Assignment	Module 4 Exercises Solutions to selected practical exercises	10%	- Due date: 03 Oct 2022 at 23:59	3-5 pages
Outcomes assessed:
Assignment	Module 5 Exercises Solutions to selected practical exercises	10%	- Due date: 17 Oct 2022 at 23:59	3-5 pages
Outcomes assessed:
Assignment	Major Assignment 2 Major assignment covering Modules 1 to 6	30%	- Due date: 31 Oct 2022 at 23:59	10-12 pages
Outcomes assessed:

Assessment summary

A pre-recorded presentation task covering the Module 1 and 2 exercises
Submission of solutions to selected practical exercises for Modules 3, 4 and 5
Major Assignment 1 is a written assignment covering Modules 1-3
Major Assignment 2 is a written assignment covering Modules 1-6

Detailed information for each assessment can be found on Canvas

Assessment criteria

Grade	Mark Range	Description
AF Absent fail	Range from 0 to 49	To be awarded to students who fail to demonstrate the learning outcomes for the unit at an acceptable standard through failure to submit or attend compulsory assessment tasks or to attend classes to the required level.
FA Fail	Range from 0 to less than 50	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty.
PS Pass	Range from 50 to less than 65	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard
CR Credit	Range from 65 to less than 75	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard
D Distinction	Range from 75 to less than 85	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard
HD High distinction	Range from 85 to 100 inclusive	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard

For more information see guide to grades.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Standard BCA policy for late penalties for submitted work is a 5% deduction from the earned mark for each day the assessment is late, up to a maximum of 50%.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Module 1 - Introduction to causal concepts	Independent study (20 hr)
	Module 2 - Causal diagrams and directed acyclic graphs	Independent study (20 hr)
	Module 3 - Time-invariant exposures and propensity scores	Independent study (30 hr)
	Module 4 - Marginal structural models (MSM) for time-varying treatments	Independent study (20 hr)
	Module 5 - Causal mediation analysis	Independent study (20 hr)
	Module 6 - Complier average causal effects	Independent study (10 hr)

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

There is no single prescribed text for the subject, but a number of reference books are suggested as background material (provided on Canvas). The one that we find closest to our materials is currently in draft version and free to download:

Hernán MA, Robins JM. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC, 2020.

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. Use counterfactuals (potential outcomes) to precisely define causal effects
LO2. Describe the differences between association and causation, and the fundamental assumptions required for causation
LO3. Construct causal diagrams and use them to identify potential sources of bias
LO4. Implement causal inference methods, using software, for single time point and longitudinal exposures, and for mediation analyses
LO5. Interpret results of analyses in light of the causal assumptions required
LO6. Effectively communicate results of causal analyses in language suitable for a clinical or epidemiological journal

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.

CSI was last delivered in Semester 2, 2021. Since then, some typographical errors in the notes have been corrected. The Module 1 and 2 assessment task now takes the form of a pre-recorded presentation.

Additional information

This unit is delivered externally via the Biostatistics Collaboration of Australia (BCA).

Software requirements: You will require access to, and a working familiarity with, either Stata or R.

Biostatistics Collaboration of Australia (BCA)

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

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

Media

Student links

About us

Connect

Current students

BSTA5017: Causal Inference

Semester 2, 2022 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

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

Media

Student links

About us

Connect