Master’s in Causality and Experimentation (A/B) for Engineering
Online master's programme of 1,900 hours in causality and experimentation (A/B) for engineering: uplift, counterfactuals and trial design. 12 modules covering causal graphical models, classical design of experiments, A/B testing, experimentation in physical systems, observational and quasi-experimental inference, causal machine learning and adaptive experimentation, with ISO 3534-3, ISO 5725-2 and ISO 16269-6, and an assigned technical tutor.
Master’s in Causality and Experimentation (A/B) for Engineering — studying from Australia
Data, artificial intelligence and simulation are central to Australian industries including mining, banking, health, agriculture and government. Organisations use machine learning, digital twins and advanced analytics to improve productivity, asset performance and decision making. National science agency CSIRO, along with universities and a growing start-up ecosystem, drives research and applied innovation. Australia has also developed national AI ethics principles, making responsible design an important professional consideration. Engineers in this area typically work for technology firms, consultancies, research organisations and large enterprises.
Fees for Australia: A$12,600 (AUD), with instalments and employer sponsorship available.
Relevant bodies in Australia
Typical roles
Questions from Australia
Which Australian bodies recognise data and AI engineering skills?
Engineers Australia and the Australian Computer Society (ACS) are the main professional bodies, and the ACS runs a skills assessment for migration purposes. SEIUM's private qualifications are not accredited by either. Whether a private qualification is valued is decided by the employer or the relevant body.
What data and AI careers exist in Australia for this training?
Australia has demand for data scientists, AI engineers and simulation analysts across finance, health, mining, government and technology firms. Employers mainly assess your portfolio, technical skills and results. A SEIUM qualification may support your profile, but each employer decides its relevance.
Can I study this online from Australia while working?
Yes, the programmes are fully online and taught in English, and no student visa is needed to study from home. Specialist courses are 300 hours, postgraduate diplomas 800 and master's 1,900, with an assigned technical tutor and a final project. Fees can be paid in instalments, and employer sponsorship may be possible.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Distinguish causal relationships from spurious correlations.
Build causal graphs and determine the identifiability of effects.
Design factorial experiments and A/B tests with adequate power.
Apply causal inference methods to observational data.
Estimate heterogeneous effects with causal machine learning.
Implement adaptive experimentation and experimentation platforms.
Communicate causal conclusions to support technical decision-making.
Who it's for
- Data scientists who want to answer what caused an outcome, not just predict it.
- Experimentation engineers running A/B tests on software, devices or fleets.
- Six Sigma quality analysts looking for rigorous root-cause methods.
- Process engineers who need to prove the effect of a change on the line.
- Design of experiments specialists extending into observational and adaptive methods.
- Technical product analysts measuring the impact of new features.
- Engineering or science graduates looking to enter manufacturing.
What you'll work with
- ISO 3534-3 (statistics vocabulary — design of experiments)
- ISO 5725-2 (accuracy of measurement methods — repeatability and reproducibility)
- ISO 16269-6 (statistical interpretation of data — tolerance intervals)
- ISO 13053-1 (Six Sigma DMAIC methodology)
- ISO 11843-1 (capability of detection)
- Regulation (EU) 2016/679 (GDPR — protection of personal data)
- DoWhy
- EconML
- R
- Minitab
- Python statsmodels
12 modules
1.1Simpson's and Berkson's paradoxes1.2Confounders, colliders and mediators1.3Pearl's ladder of causation1.4Rubin's potential outcomes1.5Average and heterogeneous effects1.6SUTVA and ignorability assumptions1.7Observational versus experimental data1.8Inference errors in engineering1.9Causality in physical systems1.10Case study: false cause in quality data
2.1Directed acyclic graphs2.2d-separation2.3Back-door criterion2.4Front-door criterion2.5Do-calculus2.6Identifiability2.7Causal discovery with PC and GES2.8Structural equation models2.9Expert knowledge in the graph2.10Lab: DAG of a manufacturing process with DoWhy
3.1Fisher's principles3.2Randomisation and blocking3.3Full factorial designs3.4Fractional factorials3.5Response surfaces3.6Taguchi designs3.7ANOVA and interactions3.8Pilot plant experiments3.9Sample size and power3.10Practical exercise: factorial design of an injection moulding process
4.1Unit of randomisation4.2Primary and guardrail metrics4.3Power calculation and MDE4.4t-tests and bootstrap4.5Variance reduction with CUPED4.6Peeking problems4.7Sequential testing4.8Multiple comparisons4.9Experiments in embedded software and OTA updates4.10Lab: analysing an A/B experiment
5.1A/B testing across equipment fleets5.2Switchback experiments5.3Interference and network effects5.4Experiments in power grids5.5Testing in buildings and HVAC5.6Experiments with human operators5.7Safety constraints5.8Staggered trials5.9Costs of experimentation5.10Case study: switchback in plant control
6.1Regression adjustment6.2Propensity score matching6.3Inverse probability weighting6.4Doubly robust estimators6.5Overlap diagnostics6.6Sensitivity analysis6.7Instrumental variables6.8Regression discontinuity6.9Selection bias6.10Practical exercise: effect of a maintenance improvement
7.1Difference-in-differences7.2Parallel trends7.3Synthetic control7.4Interrupted time series7.5Bayesian CausalImpact7.6Staggered events7.7Placebos and falsification7.8Dynamic effects7.9Panel data7.10Lab: impact of a process change with synthetic control
8.1Heterogeneous effects (CATE)8.2S-, T- and X-learners8.3Double machine learning8.4Causal forests8.5Uplift modelling8.6Optimal treatment policies8.7Validating causal models8.8EconML and CausalML libraries8.9Interpreting effects8.10Practical exercise: effect-based segmentation with causal forests
9.1Exploration and exploitation9.2Epsilon-greedy and UCB9.3Thompson sampling9.4Contextual bandits9.5Bayesian parameter optimisation9.6Regret and efficiency9.7Inference after adaptive allocation9.8Risks of online learning9.9Applications in process tuning9.10Workshop: bandit for selecting machine parameters
10.1Architecture of an experimentation platform10.2Feature flags and gradual roll-out10.3Automated logging and analysis10.4Quality of experimental data10.5Sample ratio mismatch10.6Metrics catalogue10.7Experiment governance and review10.8Ethics and consent10.9A culture of evidence-based decisions10.10Case study: internal A/B platform
11.1Quality and root causes11.2Energy efficiency11.3Reliability and warranties11.4Vehicle software11.5Clinical trials of devices11.6Maintenance policies11.7Pricing and industrial products11.8Public policy evaluation11.9Reports for technical management11.10Practical exercise: causal report for a quality committee
12.1Causal question and context12.2Causal graph and assumptions12.3Experimental or quasi-experimental design12.4Data collection and preparation12.5Effect estimation12.6Sensitivity analysis12.7Heterogeneous effects12.8Decision recommendation12.9Written thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is DoWhy, EconML, R, Minitab and Python statsmodels, in free, academic or evaluation versions. Testing is studied through real procedures: A/B experiments on software and devices, factorial designs in a pilot plant and quasi-experimental evaluations with historical data. Assessment is continuous for each module, plus the final Master's Thesis.
Standards covered: ISO 3534-3, ISO 5725-2, ISO 16269-6, ISO 13053-1 (Six Sigma), ISO 11843-1, Regulation (EU) 2016/679 (GDPR).
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with DoWhy, EconML, R, Minitab and Python statsmodels, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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