# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2023/2024

### Timetable information in the Course Catalogue may be subject to change.

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# Postgraduate Course: Methods for Causal Inference (UG) (INFR11234)

 School School of Informatics College College of Science and Engineering Credit level (Normal year taken) SCQF Level 11 (Postgraduate) Availability Available to all students SCQF Credits 10 ECTS Credits 5 Summary This course follows the delivery and assessment of Methods for Causal Inference (INFR11207) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11207 instead. Course description This course follows the delivery and assessment of Methods for Causal Inference (INFR11207) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11207 instead.
 Pre-requisites It is RECOMMENDED that students have passed Machine Learning and Pattern Recognition (INFR11130) AND Probability (MATH08066) Co-requisites It is RECOMMENDED that students also take Probabilistic Modelling and Reasoning (INFR11134) Prohibited Combinations Students MUST NOT also be taking Methods for Causal Inference (INFR11207) Other requirements This course follows the delivery and assessment of Methods for Causal Inference (INFR11207) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11207 instead. This course is open to all MSc and MInf students at School of Informatics, including those on joint degrees. The course is also available to MSc students at School of Mathematics (e.g. MSc Statistics and Data Science). Maths requirements: 1. Linear algebra: Vectors, matrices: addition, multiplication, inversion, diagonalization (eigenvectors). 2. Special functions: properties and combination rules for logarithm and exponential. 3. Calculus: Differentiation and integration. 4. Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Univariate Gaussian distribution. Joint and conditional distributions. Programming requirements: Students should be familiar with programming in a modern object-oriented language, ideally Python which is the course language.
 Pre-requisites As above. High Demand Course? Yes
 Academic year 2023/24, Available to all students (SV1) Quota:  None Course Start Semester 2 Timetable Timetable Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 5, Summative Assessment Hours 2, Revision Session Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 69 ) Assessment (Further Info) Written Exam 80 %, Coursework 20 %, Practical Exam 0 % Additional Information (Assessment) Exam 80% Coursework 20% Feedback - Questions on material during the lectures will be discussed - Two feedback sessions within lecture hours, after each of the two course assignments (one formative, one summative) - Feedback on the work during tutorials - Weekly online quiz on Learn, with solutions provided after answering the questions (not assessed) - Course forum on Learn for students to ask questions Exam Information Exam Diet Paper Name Hours & Minutes Main Exam Diet S2 (April/May) Methods for Causal Inference (UG) (INFR11234) 2:00
 On completion of this course, the student will be able to: explain the difference between causal and associational estimation and justify why causal inference techniques are necessary to derive meaning from observational dataexplain the difference between randomised trials vs observational studies related to public health and other types of data more generallylearn and apply foundational causal estimation techniques using two major frameworks: (i) Rubin's Potential Outcomes and (ii) Pearls Structural (graphical) causal models to simulated examples and real world data, in the presence of observed and unobserved variablesexplain different types of causal discovery algorithm, learn their underlying assumptions and short-comings, and be able to apply them to data using available softwaremodify / repurpose a current technique in order to apply it to a particular problem of interest
 Books that may be useful but not required: 1) Causal Inference in Statistics: A Primer (Pearl, Glymour, Jewell, 2016). 2) Elements of Causal Inference: Foundations and Learning Algorithms (by Peters, Janzing and Schölkopf 3) More advanced: Causality (Pearl, 2009)
 Graduate Attributes and Skills Problem-solving, critical / analytical thinking, independent learning and cross-cultural or cross-disciplinary communication Keywords causal inference,causal discovery,data science,probability and statistics,machine learning,MCI
 Course organiser Dr Ava Khamseh Tel: (0131 6)51 1426 Email: Ava.khamseh@igmm.ed.ac.uk Course secretary Ms Lindsay Seal Tel: (0131 6)50 2701 Email: lindsay.seal@ed.ac.uk
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