Master’s in Causality and Experimentation Engineering
Online master's programme of 1,900 hours in causality and experimentation: 12 modules covering causal frameworks, causal graphs, classical design of experiments, online A/B testing, complex experiments, observational causal inference, quasi-experimental designs, causal machine learning, causal discovery and experimentation governance, with an assigned technical tutor.
Master’s in Causality and Experimentation 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$17,700 (New Zealand dollar), with instalments and employer sponsorship available.
Relevant bodies in New Zealand
Typical roles
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.
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 correlation from causation using formal frameworks.
Build and analyse causal graphs to identify effects.
Design rigorous controlled experiments and A/B tests.
Estimate causal effects from observational data.
Apply quasi-experimental methods to evaluate interventions.
Use causal machine learning to estimate heterogeneous effects.
Communicate causal results for decision-making.
Who it's for
- Causal data scientists who need to go beyond predictive models.
- Experimentation analysts designing and reading A/B tests.
- Experimentation platform engineers building assignment and metrics tooling.
- Data economists working with observational and panel data.
- Policy evaluation analysts using difference-in-differences and synthetic controls.
- Data-driven process engineers looking for the true drivers of yield and quality.
- Engineering or science graduates aiming to move into technology and digital product roles.
What you'll work with
- ISO 3534-3 (statistics vocabulary: design of experiments)
- ICH E9(R1) (estimands and sensitivity analysis in clinical trials)
- ISO 5725-2 (accuracy of measurement methods: repeatability and reproducibility)
- Regulation (EU) 2016/679 (GDPR) (EU General Data Protection Regulation)
- ISO 16355-1 (application of statistical and related methods to new technology and product development)
- ISO 20252 (market, opinion and social research)
- ICH E9 (statistical principles for clinical trials)
- Python with DoWhy
- EconML
- R with the grf package
- DAGitty
- Statsig
12 modules · 1900 h
1.1Associational, interventional and counterfactual questions1.2The ladder of causation1.3Simpson's paradox1.4Confounding, selection bias and colliders1.5Rubin's potential outcomes1.6SUTVA, ignorability and positivity assumptions1.7ATE, ATT and CATE estimands1.8Causality in engineering and product1.9Common mistakes in observational analyses1.10Case study: critical analysis of a flawed causal conclusion
2.1Directed acyclic graphs2.2d-separation and conditional independences2.3The back-door criterion2.4The front-door criterion2.5Pearl's do-calculus2.6Instrumental variables in graphs2.7Eliciting graphs with experts2.8Using the DAGitty tool2.9Graphs for industrial systems2.10Workshop: building the DAG of a production process
3.1Randomisation, replication and blocking3.2Full factorial experiments with interactions3.3Fractional designs and confounding of effects3.4Block designs and Latin squares3.5Split-plot designs3.6Response surfaces3.7Power and sample size3.8Analysis of variance3.9Experiments on the plant floor3.10Lab: factorial design in an industrial process
4.1Experimentation platforms4.2Randomisation unit and assignment4.3Primary and guardrail metrics4.4Hypothesis tests and p-values4.5Variance reduction with CUPED4.6Sequential testing and peeking4.7Correction for multiple comparisons4.8Novelty and learning effects4.9Sample ratio mismatch4.10Practical: analysing an A/B test with CUPED
5.1Interference and network effects5.2Cluster randomisation5.3Switchback experiments alternating over time5.4Experiments in two-sided marketplaces5.5Long-term experiments5.6Multi-armed bandits versus A/B tests5.7Bayesian analysis of experiments5.8Adaptive assignment designs5.9Ethics in experimentation with users5.10Workshop: switchback design for a logistics platform
6.1Regression adjustment6.2Matching and propensity scores6.3Inverse probability weighting6.4Doubly robust estimators6.5Balance and overlap diagnostics6.6Sensitivity analysis for unmeasured confounding6.7E-values for sensitivity6.8Panel data and fixed effects6.9Validation against experiments6.10Lab: effect of an intervention with propensity scores
7.1Difference-in-differences7.2Staggered difference-in-differences7.3The synthetic control method7.4Regression discontinuity designs7.5Instrumental variable estimation7.6Interrupted time series7.7Bayesian causal impact7.8Placebo tests7.9Public policy evaluation7.10Practical: synthetic control of a regulatory change
8.1Heterogeneous treatment effects8.2S-, T- and X-meta-learners8.3Double machine learning8.4Causal forests and heterogeneous effects8.5Uplift models8.6Optimal treatment policies8.7Validating causal models8.8The DoWhy and EconML libraries8.9Interpretability of effects8.10Lab: campaign uplift with EconML
9.1Faithfulness and sufficiency assumptions9.2The constraint-based PC algorithm9.3Score-based GES methods9.4LiNGAM functional models9.5Causality in time series and Granger causality9.6The PCMCI algorithm for time series9.7Incorporating expert knowledge9.8Evaluating discovered graphs9.9Limitations of causal discovery9.10Workshop: causal discovery in sensor data
10.1Graph-based root cause analysis10.2Causal fault diagnosis10.3Structural causal models of processes10.4Counterfactuals in digital twins10.5Attribution in microservice systems10.6Causality in reliability10.7Process optimisation with causal effects10.8Transfer between plants10.9Model robustness under interventions10.10Case study: root cause of a yield loss
11.1Experimentation maturity in organisations11.2Experiment design review11.3Pre-registration and reproducibility11.4Communicating causal results11.5Ethics and research involving people11.6Data protection in experiments11.7Metric governance11.8Meta-analysis of experiments11.9Mistakes and lessons from industry11.10Practical: experiment report for senior management
12.1Causal question and context12.2Causal graph and assumptions12.3Experimental or quasi-experimental design12.4Data and preparation12.5Effect estimation12.6Sensitivity analysis12.7Decision recommendations12.8Conclusions and causal limitations12.9Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable materials, 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 Python with DoWhy, EconML, R with the grf package, DAGitty and Statsig, in free, academic or evaluation versions. Real procedures are studied, such as designing and analysing A/B tests, policy evaluation with synthetic controls and root cause analysis with causal graphs. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 3534-3, ICH E9(R1), ISO 5725-2, Regulation (EU) 2016/679 (GDPR), ISO 16355-1, ISO 20252, ICH E9.
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 Python with DoWhy, EconML, R with the grf package, DAGitty and Statsig, 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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