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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.

Duration12–14 months

ModeOnline

LanguageEnglish

Start datesSet by the faculty

AwardSEIUM Institute diploma

Enrol now View syllabus Ask an advisor

Master’s in Causality and Experimentation (A/B) for Engineering — studying from New Zealand

New Zealand's data and artificial intelligence sector is growing, with strong activity in Auckland, Wellington and Christchurch across fintech, agritech, health analytics and government data services. Universities including the University of Auckland, the University of Otago and Victoria University of Wellington run leading research in machine learning and computational modelling. Simulation and analytics skills are widely applied in agriculture, energy and environmental modelling, and employers include government agencies such as Stats NZ as well as technology firms and consultancies.

Fees for New Zealand: NZ$15,700 (New Zealand dollar), with instalments and employer sponsorship available.

Relevant bodies in New Zealand

Stats NZ (Tatauranga Aotearoa)
Digital Council for Aotearoa New Zealand
Institute of Electrical and Electronics Engineers (IEEE) New Zealand Section

Typical roles

Data Scientist
Machine Learning Engineer
Simulation Engineer
Data Analyst

Questions from New Zealand

Are data and AI engineering roles regulated in New Zealand, and how does that affect a private qualification?

Data and AI are not regulated professional titles in New Zealand, though privacy obligations fall under the Office of the Privacy Commissioner. Engineers who want chartered status can pursue registration through Engineering New Zealand. SEIUM's qualifications are private, not state degrees, and not accredited by a national body, so employers decide how much they value them.

Which New Zealand employers hire data, AI and simulation engineers?

Demand is found in banks and insurers, government agencies, healthcare, energy and utilities, technology firms, and consultancies. Employers usually assess portfolios, practical projects and technical interviews more than credentials. A private online programme can help build relevant skills, but it does not guarantee a position or a specific salary.

Can I complete this online from New Zealand in English while working?

Yes. The programmes are fully online and taught in English, so you can study from New Zealand without a student visa. You will have an assigned technical tutor and complete a final project. Specialist courses are 300 hours, diplomas 800 and master's 1,900. Fees can be paid in instalments, and some employers sponsor study, so ask yours early.

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Learning outcomes

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.

Profile

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.

Standards & tools

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

Syllabus

12 modules

1.1Simpson's and Berkson's paradoxes
1.2Confounders, colliders and mediators
1.3Pearl's ladder of causation
1.4Rubin's potential outcomes
1.5Average and heterogeneous effects
1.6SUTVA and ignorability assumptions
1.7Observational versus experimental data
1.8Inference errors in engineering
1.9Causality in physical systems
1.10Case study: false cause in quality data

2.1Directed acyclic graphs
2.2d-separation
2.3Back-door criterion
2.4Front-door criterion
2.5Do-calculus
2.6Identifiability
2.7Causal discovery with PC and GES
2.8Structural equation models
2.9Expert knowledge in the graph
2.10Lab: DAG of a manufacturing process with DoWhy

3.1Fisher's principles
3.2Randomisation and blocking
3.3Full factorial designs
3.4Fractional factorials
3.5Response surfaces
3.6Taguchi designs
3.7ANOVA and interactions
3.8Pilot plant experiments
3.9Sample size and power
3.10Practical exercise: factorial design of an injection moulding process

4.1Unit of randomisation
4.2Primary and guardrail metrics
4.3Power calculation and MDE
4.4t-tests and bootstrap
4.5Variance reduction with CUPED
4.6Peeking problems
4.7Sequential testing
4.8Multiple comparisons
4.9Experiments in embedded software and OTA updates
4.10Lab: analysing an A/B experiment

5.1A/B testing across equipment fleets
5.2Switchback experiments
5.3Interference and network effects
5.4Experiments in power grids
5.5Testing in buildings and HVAC
5.6Experiments with human operators
5.7Safety constraints
5.8Staggered trials
5.9Costs of experimentation
5.10Case study: switchback in plant control

6.1Regression adjustment
6.2Propensity score matching
6.3Inverse probability weighting
6.4Doubly robust estimators
6.5Overlap diagnostics
6.6Sensitivity analysis
6.7Instrumental variables
6.8Regression discontinuity
6.9Selection bias
6.10Practical exercise: effect of a maintenance improvement

7.1Difference-in-differences
7.2Parallel trends
7.3Synthetic control
7.4Interrupted time series
7.5Bayesian CausalImpact
7.6Staggered events
7.7Placebos and falsification
7.8Dynamic effects
7.9Panel data
7.10Lab: impact of a process change with synthetic control

8.1Heterogeneous effects (CATE)
8.2S-, T- and X-learners
8.3Double machine learning
8.4Causal forests
8.5Uplift modelling
8.6Optimal treatment policies
8.7Validating causal models
8.8EconML and CausalML libraries
8.9Interpreting effects
8.10Practical exercise: effect-based segmentation with causal forests

9.1Exploration and exploitation
9.2Epsilon-greedy and UCB
9.3Thompson sampling
9.4Contextual bandits
9.5Bayesian parameter optimisation
9.6Regret and efficiency
9.7Inference after adaptive allocation
9.8Risks of online learning
9.9Applications in process tuning
9.10Workshop: bandit for selecting machine parameters

10.1Architecture of an experimentation platform
10.2Feature flags and gradual roll-out
10.3Automated logging and analysis
10.4Quality of experimental data
10.5Sample ratio mismatch
10.6Metrics catalogue
10.7Experiment governance and review
10.8Ethics and consent
10.9A culture of evidence-based decisions
10.10Case study: internal A/B platform

11.1Quality and root causes
11.2Energy efficiency
11.3Reliability and warranties
11.4Vehicle software
11.5Clinical trials of devices
11.6Maintenance policies
11.7Pricing and industrial products
11.8Public policy evaluation
11.9Reports for technical management
11.10Practical exercise: causal report for a quality committee

12.1Causal question and context
12.2Causal graph and assumptions
12.3Experimental or quasi-experimental design
12.4Data collection and preparation
12.5Effect estimation
12.6Sensitivity analysis
12.7Heterogeneous effects
12.8Decision recommendation
12.9Written thesis report
12.10Master's Thesis: defence before a panel

Methodology, software & labs

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).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.

Standards-based learning:guided reading of ISO 3534-3, ISO 5725-2 and ISO 16269-6 applied to real cases, not as a theoretical annex.

Industry software:DoWhy, EconML, R, Minitab and Python statsmodels, in free, academic or evaluation versions.

Master's Thesis with defence:A causal study of a real engineering question, from the causal graph and experimental design to effect estimation and a decision recommendation, defended before a panel.

Flexible format:100% online, no fixed timetables, compatible with full-time work.

Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Causal graph

Build the DAG of a real process and check which effects are identifiable.

Factorial experiment

Design and analyse a factorial experiment on a production process.

Observational estimate

Estimate the effect of an intervention from historical data with propensity and doubly robust methods.

Heterogeneous effects

Segment units by treatment effect with causal forests and propose a treatment policy.

Master's Thesis

A complete causal study with sensitivity analysis and a decision recommendation, defended before a panel.

Career opportunities

Where this takes you

Causal data scientistExperimentation engineerSix Sigma quality analystProcess engineerDesign of experiments specialistTechnical product analyst

Admissions & scholarships

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.

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FAQ

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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