Advanced Causal Inference

Lecturer

Dr. Urmi Ninad
Dr. Martin Rabel

General information

Semester: SO 2026
hrs/wk: 4
ECTS: 6
Registration Time: 01/04/2026 - 30/04/2026
Course type: Lecture/Exercise (L/E)
Lecturer Language: Englisch

Study programs, module groups & modules

M.Sc. Computer Science
- Specialised Studies
  - II Track: Algorithms and Foundations
    - Deep Dive
      - HPI-CS-AAD: Applied Algorithms - Deep Dive
    - Specialization
      - HPI-CS-AAS: Applied Algorithms - Specialization
  - V Track: Security Engineering
    - Deep Dive
      - HPI-CS-DAD: Data Systems - Deep Dive
  - I Track: Data and AI
    - Specialization
      - HPI-CS-PMS: Probabilistic Machine Learning - Specialization
    - Deep Dive
      - HPI-CS-PMD: Probabilistic Machine Learning - Deep Dive
- Mandatory Modules
  - V Track: Security Engineering
  - I Track: Data and AI
  - II Track: Algorithms and Foundations

More information

Workload

Lecture: Monday 10am - 12am, 2.70.0.08

Excercise: Monday 2pm - 4pm, 2.70.0.08

Note

This advanced course builds on the basic causal inference class by extending theory and tackling real-world data complexities with modern methods. It deepens understanding of conditional independence testing (CIT) and develops causal discovery (CD), focusing on hidden confounders, cycles, non-stationarity, multiple datasets, and high-dimensional variables. While emphasizing constraint-based CD, score-based algorithms are also covered within a broader framework. We address methodological advances, benchmarking, and inductive biases. Beyond CD as a first stage, the course studies causal effect identification, estimation under finite samples, counterfactuals, and mediation. Connections to potential outcomes, dynamic systems, and representation learning are drawn. Theory is paired with applications, proofs, and real data examples across scientific fields.