Diploma in Applied Causal Inference (DAGs, IV, DID)
Online postgraduate diploma of 800 hours in applied causal inference (DAGs, IV, DID). 8 modules covering potential outcomes, directed acyclic graphs, confounder adjustment and double machine learning, instrumental variables, difference-in-differences, causality in time series and transparent causal reporting in line with STROBE, ICH E9(R1) and ISO 3534-3, with an assigned technical tutor.
Diploma in Applied Causal Inference (DAGs, IV, DID) — studying from the United Kingdom
Data science, artificial intelligence and simulation are central to engineering practice across the United Kingdom, with strong hubs in London, Cambridge, Oxford, Bristol and Edinburgh. Universities, research institutes such as the Alan Turing Institute and industrial users in aerospace, automotive, energy and finance demand skills in modelling, machine learning and computational analysis. Engineering simulation tools are widely used for design validation and digital twins. Organisations seek professionals who can turn large datasets into engineering decisions while meeting data protection and governance requirements.
Fees for the United Kingdom: £1,250 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Which UK bodies recognise data and AI qualifications?
In the UK, the British Computer Society (BCS), the Royal Statistical Society (RSS) and the Institute of Analytics are relevant professional bodies, and the IET covers many engineering-related AI and data areas. SEIUM's qualifications are private and not accredited by these bodies. Check directly with BCS or RSS whether your study would be considered towards their membership or certification.
Do UK employers in data and AI value private online qualifications?
UK employers in finance, retail, health, technology and public sector analytics typically assess candidates on technical skills, portfolio work and experience with real data. A private qualification may help demonstrate specialist learning, but its value depends on each employer's judgement. Publishing projects and showing practical results will usually matter most.
Can I study this online from the UK alongside work, and what English level is required?
Yes, the programmes are fully online and can be studied from anywhere in the UK without a student visa. Teaching is in English, so you need a solid working level of technical English. An assigned technical tutor will support you. Check the programme hours against your working commitments and ask about instalment payments or employer sponsorship.
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 the potential outcomes framework.
Build and analyse directed acyclic graphs.
Adjust for confounding with propensity scores, IPW and double machine learning.
Apply instrumental variables with diagnostics of their assumptions.
Evaluate interventions with difference-in-differences and synthetic control.
Analyse causality in time series and dynamic systems.
Communicate causal results with explicit assumptions and robustness checks.
Who it's for
- Data scientists focused on causal questions.
- Impact analysts.
- Process engineers with an analytical profile who need to know what really drives plant results.
- Quantitative researchers.
- Continuous improvement analysts.
- Advanced analytics consultants.
- Engineering or science graduates seeking to enter manufacturing.
What you'll work with
- STROBE guidelines (Observational studies)
- ICH E9(R1) (Estimands and sensitivity analysis)
- ISO 3534-3 (Vocabulary: design of experiments)
- ISO/IEC 42001:2023 (AI management systems)
- ISO 13053-1 (Six Sigma: DMAIC methodology)
- DoWhy
- EconML
- DAGitty
- R with the did and CausalImpact packages
- Python linearmodels
8 modules
1.1Correlation, confounding and causation1.2Simpson's paradox1.3Rubin's potential outcomes model1.4Average treatment effect (ATE and ATT)1.5SUTVA, positivity and exchangeability assumptions1.6Hierarchy of evidence1.7Observational studies in engineering1.8Internal and external validity1.9Well-formulated causal questions1.10Workshop: formulating causal questions on the shop floor
2.1Pearl's structural causal models2.2Chains, forks and colliders2.3Conditional independences and d-separation2.4Back-door criterion2.5Front-door criterion2.6Collider and selection bias2.7The do-operator and do-calculus2.8Building DAGs with experts2.9DAGitty and DoWhy tools2.10Practical exercise: DAG of a heat treatment process
3.1Regression with covariate adjustment3.2Propensity score matching3.3Inverse probability weighting (IPW)3.4Doubly robust estimators3.5Balance checking3.6Overlap and trimming3.7Double machine learning3.8Heterogeneous effects with causal forests3.9Sensitivity analysis for unmeasured confounding3.10Lab: IPW and DML with EconML
4.1Intuition and assumptions of an instrument4.2Relevance, exclusion and independence4.3Two-stage least squares4.4Weak instruments and diagnostics4.5Local average treatment effect (LATE)4.6Instruments in industrial contexts4.7Designs with imperfect randomisation4.8Regression discontinuity as a related case4.9Limitations and criticisms4.10Practical exercise: 2SLS estimation in linearmodels
5.1Before-after design with a control group5.2Parallel trends assumption5.3Estimation with fixed effects5.4Pre-trend tests5.5Staggered adoption and its problems5.6Callaway-Sant'Anna estimators5.7Synthetic control method5.8Interrupted time series5.9Inference with few groups5.10Case study: process change across several plants
6.1Granger causality and its limits6.2Bayesian structural time-series models (CausalImpact)6.3Causal discovery with PC and FCI6.4LiNGAM linear non-Gaussian models6.5Causality in systems with feedback6.6Interventions in controlled processes6.7Integrating physical knowledge6.8Validating discovered graphs6.9Risks of over-interpretation6.10Lab: CausalImpact on energy consumption
7.1Transparent causal reporting7.2Explicit assumptions and robustness checks7.3Refutation with placebos7.4Visualising effects and uncertainty7.5From estimated effect to decision7.6Ethics and responsibility in conclusions7.7Peer review7.8Integration with experimentation7.9Applied cases in industry7.10Workshop: refuting an analysis with DoWhy
8.1Causal question and context8.2Inventory of available data8.3DAG of the problem8.4Identification strategy8.5Effect estimation8.6Robustness checks8.7Interpretation and decision8.8Limitations and external validity8.9Report and presentation8.10Final project: final review with the tutor
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 is compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is DoWhy, EconML, DAGitty, R with the did and CausalImpact packages and Python linearmodels, in free, academic or evaluation versions. Practical work is based on real procedures for estimating effects with observational plant data, difference-in-differences analysis with staggered adoption and refutation and sensitivity tests. Assessment is continuous by module plus the final project.
Standards covered: STROBE guidelines, ICH E9(R1), ISO 3534-3, ISO/IEC 42001:2023, ISO 13053-1.
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
- Prior technical training or professional experience in the sector. No university degree is required.
- 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 800 hours over 6–8 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, DAGitty, R with the did and CausalImpact packages and Python linearmodels, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme 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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