Diploma in Uplift Modeling and Causality-Based Decision Making
Online postgraduate diploma of 800 hours in uplift modelling and causality-based decision-making. 8 modules covering potential outcomes and causal graphs, experimental design, meta-learners, causal trees and forests, Qini evaluation, quasi-experimental methods and budget-constrained treatment policies in line with the GDPR, the EU AI Act and CONSORT 2010, with an assigned technical tutor.
Diploma in Uplift Modeling and Causality-Based Decision Making — studying from the United States
Data science, artificial intelligence and simulation are central to American industry, with major concentrations in the San Francisco Bay Area, Seattle, New York and Boston. Engineering organizations increasingly rely on machine learning, digital twins and high-performance computing to accelerate design, testing and operations. Employers range from large technology companies to aerospace, automotive and energy firms that apply analytics to complex systems. Demand is strong for engineers who combine domain knowledge with modeling, statistics and software skills.
Fees for the United States: US$1,440 (US Dollars), with instalments and employer sponsorship available.
Relevant bodies in the United States
Typical roles
Questions from the United States
Which US bodies relate to data science, AI and simulation engineering?
There is no single licensing body for data science or AI in the US. Relevant professional organizations include the IEEE Computer Society, the Association for Computing Machinery (ACM), and the Society for Industrial and Applied Mathematics (SIAM). Federal guidance such as the NIST AI Risk Management Framework shapes practice. SEIUM's qualifications are private and not accredited by a national body, so recognition is decided by employers.
How do US employers in data and AI regard private online postgraduate qualifications?
US tech firms, financial institutions and research labs generally hire on the basis of portfolios, technical interviews, relevant experience and sometimes advanced degrees. A private online qualification can show structured study, but it is not a standard hiring requirement and each employer weighs it differently. Public projects, code repositories and measurable results tend to matter most.
Is it realistic to study data and AI from the US around a full-time job?
Yes, the programmes are fully online and taught in English, so you can study from home without a student visa. The workload is large: about 1,900 hours for a master's, 800 hours for a postgraduate diploma, and 300 hours for a specialist course. An assigned technical tutor supports you. Fees may be paid in instalments, and employer sponsorship is sometimes available, which you should confirm with your employer.
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
Frame decision problems in terms of potential outcomes and causal graphs.
Design randomised experiments with adequate power and control groups.
Estimate heterogeneous effects with meta-learners and causal forests.
Evaluate uplift models with Qini curves, AUUC and policy value.
Apply quasi-experimental methods when randomisation is not possible.
Optimise treatment allocation under budget constraints.
Communicate incremental impact rigorously and ethically.
Who it's for
- Data scientists working on causal problems.
- Marketing analytics analysts who need to measure true incremental impact.
- Experimentation leads.
- Machine learning engineers.
- Pricing and promotions consultants.
- Business decision analysts.
- Engineering or science graduates seeking to enter e-commerce.
What you'll work with
- Regulation (EU) 2016/679 (GDPR)
- Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
- Organic Law 3/2018 (LOPDGDD, Spanish data protection and digital rights law)
- ISO/IEC 22989:2022 (AI concepts and terminology)
- ISO/IEC 23053:2022 (Framework for AI systems using machine learning)
- ISO/IEC 25012:2008 (Data quality model)
- CONSORT 2010 Statement (Reporting of randomised trials)
- CausalML
- EconML
- DoWhy
- grf (R)
- scikit-uplift
8 modules
1.1Neyman-Rubin potential outcomes model1.2ATE, ATT and CATE effects1.3SUTVA, ignorability and positivity assumptions1.4Directed acyclic graphs and d-separation1.5Backdoor criterion and confounder adjustment1.6Simpson's paradox and selection bias1.7Randomised experiments versus observational data1.8Counterfactuals in Pearl's hierarchy1.9Identifiability and the rules of do-calculus1.10Workshop: DAG of a retention campaign with DAGitty
2.1Simple, stratified and block randomisation2.2Sample size and power calculation2.3Holdout groups and contamination between arms2.4Multivariate and factorial A/B designs2.5CUPED and variance reduction2.6Interference and cluster randomisation2.7Sequential tests and early stopping2.8Pre-registration and experiment logging2.9Ethics of experimentation with customers2.10Practical exercise: coupon test with a holdout group
3.1S-learner and regularisation bias3.2T-learner with independent models3.3X-learner for unbalanced treatments3.4R-learner and Robinson orthogonalisation3.5Doubly robust DR-learner3.6Choosing the base model with gradient boosting3.7Propensity estimation and weight trimming3.8CATE-specific cross-validation3.9Comparison of learners on synthetic data3.10Lab: meta-learners with CausalML on the Hillstrom dataset
4.1Uplift trees with KL divergence and chi-squared4.2Honest causal trees by Athey and Imbens4.3Generalised random forests4.4Confidence intervals for the CATE4.5Variable importance in heterogeneity4.6Chernozhukov double machine learning4.7Orthogonal estimators in EconML4.8Jaskowski and Jaroszewicz class transformation4.9Interpreting effects with SHAP values4.10Lab: causal forest with EconML and grf
5.1Uplift curves and Qini curves5.2Qini coefficient and AUUC5.3Incremental gain charts by decile5.4Out-of-sample and temporal stability5.5IPW-weighted metrics with observational data5.6Policy value and off-policy evaluation5.7Calibration of estimated effects5.8Common mistakes when evaluating uplift5.9Comparison with response propensity models5.10Practical exercise: Qini curves for an email campaign
6.1Propensity score matching6.2Inverse probability weighting6.3Difference-in-differences estimator6.4Synthetic control method6.5Instrumental variables and LATE6.6Regression discontinuity designs6.7Sensitivity analysis and E-value6.8Refuting estimates with DoWhy6.9Censoring and missing data in effects6.10Case study: impact of a loyalty programme
7.1Optimal allocation under budget constraints7.2Persuadable, sure thing, lost cause and do-not-disturb segments7.3Multiple treatments with different costs7.4Contextual bandits and Thompson sampling7.5Policy learning with decision trees7.6Fairness and discrimination in allocation7.7Deployment and monitoring of causal drift7.8Incremental return and ROI calculation7.9Communicating results to management7.10Case study: discount allocation with positive ROI
8.1Defining the action and the outcome8.2Data preparation and treatment variable8.3Experimental or observational strategy8.4Training and comparing learners8.5Qini evaluation and policy value8.6Sensitivity analysis of the estimate8.7Deployment and monitoring plan8.8Quantified business recommendation8.9Report 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 CausalML, EconML, DoWhy, grf (R) and scikit-uplift, in free, academic or evaluation versions. Practical work is based on real procedures for retention campaigns with a holdout control group, coupon allocation in e-commerce and impact evaluation with historical CRM data. Assessment is continuous by module plus the final project.
Standards covered: Regulation (EU) 2016/679, Regulation (EU) 2024/1689, Organic Law 3/2018, ISO/IEC 22989:2022, ISO/IEC 23053:2022, ISO/IEC 25012:2008, CONSORT 2010 Statement.
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 CausalML, EconML, DoWhy, grf (R) and scikit-uplift, 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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