DoctorateFaculty of Science & Applied Sciences

PhD in Causal Inference and Statistical Learning Theory

Welcome, future masters of cause and effect. We will not just learn to predict the future, but to understand the forces that shape it. Let's build the systems that can truly understand the world.

Meet the academic team Explore the faculty

Identity only. No score is printed. Checkout waits.

Level: Doctorate
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

NXAcademic
Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Causal Inference, Statistical Learning Theory, Machine Learning, Data-Driven Decision Systems

Practical focus

Statistical Consulting, Data Visualization, Data Storytelling, Public Policy Analysis

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

The World Bank
Internships in data analysis for development projects
McKinsey & Company
Opportunities in business and data analytics
The United Nations
Internships in data for sustainable development goals

Career opportunities

Senior Data Scientist
In a tech, finance, or government
Business Intelligence Architect
Designing data systems for businesses
Policy Analyst
Using data to inform government or NGO decisions
Research Scientist
In academia or industry

Jobs and projects

Problem-Solving
Applying a systematic approach to data-driven challenges
Programming
Competency in statistical languages like R or Python
Attention to Detail
Meticulousness in data cleaning and analysis
Ethical Reasoning
Understanding the ethical implications of data use

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- You will gain the skills to not only analyze data but to use it to create powerful models that can guide strategic decisions. You will become a trusted advisor in the world of data-driven decision-making.
Skills you build
- Statistical Learning: Proficiency in advanced machine learning and statistical models.
- Causal Inference: Understanding the cause-and-effect relationships in data.
- Decision Theory: Applying statistical principles to make optimal decisions.
- Big Data Analytics: Skills in extracting valuable patterns from large datasets.

Listed courses

Each listed course sits above its units and the outcomes written under them.

PhD in Causal Inference and Statistical Learning Theory

01Advanced Causal Inference
1. FoundationsFoundations of Advanced Causal Inference
  The learner can you will gain the skills to not only analyze data but to use it to create powerful models that can guide strategic decisions, as applied to Advanced Causal Inference.
  The learner can you will become a trusted advisor in the world of data-driven decision-making, as applied to Advanced Causal Inference.
2. MethodsMethods in Advanced Causal Inference
  The learner can apply a method from Advanced Causal Inference to a documented case.
  The learner can select an appropriate method from Advanced Causal Inference for a stated problem.
3. ApplicationApplication of Advanced Causal Inference
  The learner can evaluate a practice of Advanced Causal Inference against a stated criterion.
  The learner can transfer Advanced Causal Inference to a new documented context.
02Statistical Learning Theory
1. FoundationsFoundations of Statistical Learning Theory
  The learner can explain the core terms of Statistical Learning Theory.
  The learner can distinguish related ideas inside Statistical Learning Theory.
2. MethodsMethods in Statistical Learning Theory
  The learner can apply a method from Statistical Learning Theory to a documented case.
  The learner can select an appropriate method from Statistical Learning Theory for a stated problem.
3. ApplicationApplication of Statistical Learning Theory
  The learner can evaluate a practice of Statistical Learning Theory against a stated criterion.
  The learner can transfer Statistical Learning Theory to a new documented context.
03Decision Systems and Applied Statistics
1. FoundationsFoundations of Decision Systems and Applied Statistics
  The learner can explain the core terms of Decision Systems and Applied Statistics.
  The learner can distinguish related ideas inside Decision Systems and Applied Statistics.
2. MethodsMethods in Decision Systems and Applied Statistics
  The learner can apply a method from Decision Systems and Applied Statistics to a documented case.
  The learner can select an appropriate method from Decision Systems and Applied Statistics for a stated problem.
3. ApplicationApplication of Decision Systems and Applied Statistics
  The learner can evaluate a practice of Decision Systems and Applied Statistics against a stated criterion.
  The learner can transfer Decision Systems and Applied Statistics to a new documented context.
04Ethics and AI in Decision-Making
1. FoundationsFoundations of Ethics and AI in Decision-Making
  The learner can explain the core terms of Ethics and AI in Decision-Making.
  The learner can distinguish related ideas inside Ethics and AI in Decision-Making.
2. MethodsMethods in Ethics and AI in Decision-Making
  The learner can apply a method from Ethics and AI in Decision-Making to a documented case.
  The learner can select an appropriate method from Ethics and AI in Decision-Making for a stated problem.
3. ApplicationApplication of Ethics and AI in Decision-Making
  The learner can evaluate a practice of Ethics and AI in Decision-Making against a stated criterion.
  The learner can transfer Ethics and AI in Decision-Making to a new documented context.
05Research Seminar in Causal Inference
1. FoundationsFoundations of Research Seminar in Causal Inference
  The learner can explain the core terms of Research Seminar in Causal Inference.
  The learner can distinguish related ideas inside Research Seminar in Causal Inference.
2. MethodsMethods in Research Seminar in Causal Inference
  The learner can apply a method from Research Seminar in Causal Inference to a documented case.
  The learner can select an appropriate method from Research Seminar in Causal Inference for a stated problem.
3. ApplicationApplication of Research Seminar in Causal Inference
  The learner can evaluate a practice of Research Seminar in Causal Inference against a stated criterion.
  The learner can transfer Research Seminar in Causal Inference to a new documented context.
06Data Visualization Workshops
1. FoundationsFoundations of Data Visualization Workshops
  The learner can explain the core terms of Data Visualization Workshops.
  The learner can distinguish related ideas inside Data Visualization Workshops.
2. MethodsMethods in Data Visualization Workshops
  The learner can apply a method from Data Visualization Workshops to a documented case.
  The learner can select an appropriate method from Data Visualization Workshops for a stated problem.
3. ApplicationApplication of Data Visualization Workshops
  The learner can evaluate a practice of Data Visualization Workshops against a stated criterion.
  The learner can transfer Data Visualization Workshops to a new documented context.
07Statistical Consulting Case Studies
1. FoundationsFoundations of Statistical Consulting Case Studies
  The learner can explain the core terms of Statistical Consulting Case Studies.
  The learner can distinguish related ideas inside Statistical Consulting Case Studies.
2. MethodsMethods in Statistical Consulting Case Studies
  The learner can apply a method from Statistical Consulting Case Studies to a documented case.
  The learner can select an appropriate method from Statistical Consulting Case Studies for a stated problem.
3. ApplicationApplication of Statistical Consulting Case Studies
  The learner can evaluate a practice of Statistical Consulting Case Studies against a stated criterion.
  The learner can transfer Statistical Consulting Case Studies to a new documented context.
08Professional Skills for Statisticians
1. FoundationsFoundations of Professional Skills for Statisticians
  The learner can explain the core terms of Professional Skills for Statisticians.
  The learner can distinguish related ideas inside Professional Skills for Statisticians.
2. MethodsMethods in Professional Skills for Statisticians
  The learner can apply a method from Professional Skills for Statisticians to a documented case.
  The learner can select an appropriate method from Professional Skills for Statisticians for a stated problem.
3. ApplicationApplication of Professional Skills for Statisticians
  The learner can evaluate a practice of Professional Skills for Statisticians against a stated criterion.
  The learner can transfer Professional Skills for Statisticians to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

I am a statistician who believes that the ultimate frontier of data science is not just prediction, but understanding cause and effect. My work focuses on developing new theories and tools for causal discovery and machine learning, ensuring that our decisions are based on sound, evidence-based reasoning.

Applied mentorship

I am a statistician who is passionate about using data to make real-world impacts in business and public policy. My mentorship focuses on helping students apply their skills to clinical and research problems, turning raw data into actionable insights.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

• Blog Post: 'Beyond Prediction: The Dawn of Causal AI' - An accessible essay on the growing field of causal inference and its importance for the future of AI. · • Blog Post: 'The Statistical Fallacies of A/B Testing' - Discusses the common pitfalls of A/B testing and how causal inference can lead to better business decisions. · • Conference Paper: 'A New Method for Causal Discovery in Time-Series Data' - Presented at the International Conference on Machine Learning, detailing a breakthrough in understanding causality in complex systems. · • Journal Article: 'Causal Inference for Fair and Transparent Machine Learning' - Published in the Journal of Statistical Science, this paper provides a framework for designing AI systems that are both powerful and ethical. · • Standard Article: 'The Rise of Causal Analytics in Public Policy' - A multi-part series for a policy magazine on how causal models are transforming public policy. · • Book: 'The Causal Inference Handbook' - A comprehensive textbook on the theoretical and practical applications of causal inference. · • Total Score: 29/30 · • Kairos Badge: 🥇

R / 02

Mentor practice lens

• Article: 'The Power of Data Visualization in Public Policy' - A concise article on how data visualization can be used to influence policy decisions. · • Guide: 'A Beginner's Guide to Statistical Consulting' - A practical tutorial for new students on the basics of a key area of applied statistics. · • Essay: 'Building Ethical AI for Public Policy' - An essay on the challenges and responsibilities of creating AI tools for government. · • Total Score: 28/30 · • Kairos Badge: 🥇

Adaptive capability

Professor superpower

GAF-powered Causal Pathway Visualization

Adaptive capability

Mentor superpower

GAF-powered Data Narrative Construction

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Kenneth Rowntree

Causal Inference, Statistical Learning Theory, Machine Learning, Data-Driven Decision Systems

Meet your professor Open the classroom

AI Super Mentor

Dr. Marianne Dubois

Statistical Consulting, Data Visualization, Data Storytelling, Public Policy Analysis

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Adapted. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master	Doctorate This programme
9 months · Fast track	12000 EUR	9600 EUR	12000 EUR
12 months · Recommended	14400 EUR	12000 EUR	14400 EUR
15 months · Standard	16800 EUR	14400 EUR	16800 EUR
18 months · Flexible	19200 EUR	16800 EUR	19200 EUR
21 months · Extended	21600 EUR	19200 EUR	21600 EUR
24 months · Part-time	24000 EUR	21600 EUR	24000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

Named tuition lists Add-on services

Continue exploring

Find the program that expands your universe.

Browse all programs

PhD in Causal Inference and Statistical Learning Theory

Academic focus

Practical focus

The World Bank

McKinsey & Company

The United Nations

Senior Data Scientist

Business Intelligence Architect

Policy Analyst

Research Scientist

Problem-Solving

Programming

Attention to Detail

Ethical Reasoning

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Kenneth Rowntree

Dr. Marianne Dubois

Find the program that expands your universe.