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DTSTART;VALUE=DATE:20260413
DTEND;VALUE=DATE:20260418
DTSTAMP:20260414T174327
CREATED:20260119T122042Z
LAST-MODIFIED:20260309T134618Z
UID:10001767-1776038400-1776470399@ddsa.dk
SUMMARY:The Anthropological Theory of Didactics in Mathematics & Data Science Education
DESCRIPTION:Enrolment guidelines  \nThis is a specialised course where 50% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved to other applicants. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nSpecial rules apply for this course\nPlease note that all applicants will be placed on the waiting list upon registration.\nAfter registering for the course\, please send an email to the course coordinator Britta Eyrich Jessen (britta.jessen@ind.ku.dk) latest on 28 January 2026.\nThe email must contain your CV and a short description of your PhD project.\nAbout one week after registration deadline\, all applicants on the waiting list will be notified \nAim and Content\nThe course will provide a broad introduction to the anthropological theory of the didactic (ATD) as a framework for research in mathematics and data science education\, in particular didactic transposition theory\, praxeological analysis\, Herbartian schemas and Didactical Engineering based on Study and Research Paths. Moreover\, applications and methods in mathematics and data science education from primary to tertiary education will be provided and discussed. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge:\n• Acquire knowledge regarding the recent advances in the theoretical framework and methods of current ATD research\n• Gain knowledge from central texts of the ATD based literature in mathematics education (in English) \nSkills:\n• Develop the ability to recognize and validate problems within this framework\n• Apply relevant knowledge and methods when addressing the identified problems\n• \nCompetences:\n• Gain competence for and experience with writing scientific\, peer-reviewed manuscripts (that can be developed further into publications at the participants’ own initiative).\n• \nTarget Group\nPhD students in mathematics or data science education at all levels of education. \nRecommended Academic Qualifications\nTo be enrolled as PhD student with a PhD project within mathematics or data science education from primary to tertiary level education. \nResearch Area\nDidactics of mathematics (and statistics education) \nTeaching and Learning Methods\nThe course will consist of lectures followed by discussion seminars linked to each set of lectures. These will be provided in connection with participants’ presentation of their 5 pages papers applying the course literature to their own projects. \nType of Assessment\nBy February 15\, participants must submit a 5-page synopsis of their own research and how they propose to use the theoretical framework in their work (based on their own readings prior to the course)\nDuring the presence part of the course in Copenhagen\, from April 13\, 2026 9AM to April 17 at 3PM\, participants must actively participate to develop their ideas under the supervision of the course teachers and guest lecturers. This includes giving a 20 min presentation of their 5-page synopsis\nBy May 15\, participants must submit a 10-page paper on the use of the theoretical framework in their own research. \nLiterature\nThe course will be based (in part) by selected chapter from:\nY. Chevallard\, B. Barquero\, M. Bosch\, I. Florensa\, J. Gascón\, P.Nicolás\, and N. Ruiz-Munzón (Eds.\, 2022): Advances in the Anthropological Theory of the Didactic. Birkäuser Cham\nAnd\nIgnasi Florensa\, Noemí Ruiz-Munzón\, Kristina Markulin\, Berta Barquero\, Marianna Bosch\, Yves Chevallard (2024): Extended Abstracts – Proceedings of the 7th International Conference on the Anthropological Theory of the Didactic (CITAD7). Birkhäuser\, Cham. \nCourse coordinator\nAssociate professor\, Britta Eyrich Jessen \nGuest Lecturers\nThe following guest lecturers will hold lectures\, provide feedback on students’ papers and guide the discussions of their presentations during the course. The workload will be divided equally among the guest lecturers.\nProfessor Marianna Bosch (University of Barcelona\, Spain)\nProfessor Alejandro González-Martín (Université de Montréal\, Canada)\nProfessor Heidi Strømskag (Norwegian University of Science and Technology\, Norway)\nFurthermore\, the professor Carl Winsløw\, Department of Science Education\, University of Copenhagen\, be co-teaching and running the course in collaboration with the course coordinator. \nDates\nApril 13 – April 17 2026 \nExpected frequency\nNot recurrent \nCourse location\nNiels Bohr Building\, Jagtvej 155A\, 2200 Copenhagen N\, \nCourse fee\n• Participant fee: 2000 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 6000 DKK\n• PhD student from foreign university: 6000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 6000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 6000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 16.800 \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/__trashed-137/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260420
DTEND;VALUE=DATE:20260425
DTSTAMP:20260414T174327
CREATED:20260119T122748Z
LAST-MODIFIED:20260309T135225Z
UID:10001731-1776643200-1777075199@ddsa.dk
SUMMARY:International School of Chemometrics - BASICS
DESCRIPTION:Enrolment guidelines  \nThis is a specialised course where 50% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 50% of the seats are reserved to other applicants. Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nThe ISC-2026 is a four-week school designed to introduce different key aspects of Data Science and Machine Learning in different branches of science (chemistry\, food & feed\, physics\, environmental\, political economics\, etc).\nIt is addressed to BSc\, MSc\, PhD students/post-docs\, professors\, as well as industrial and private researchers.\nIMPORTANT: The ISC-2026 is structured in FOUR different and independent modules: PROGAMMING\, BASICS\, INTERMEDIATE\, CHALLENGES.\nThe students CAN CHOOSE WHICH ONES TO DO.\nPlease make sure to register individually for each course you intend to participate in. \nBASICS MODULE: \nA basic introduction to Chemometrics\, data types\, data pre-processing\, PCA\, Multivariate Linear Regression\, and Linear Algebra\nThis seminar contains several general topics:\n– PCA – PREPRO – REGRESS: Data exploration and regression. Principal Component Analysis has become the most powerful and versatile tool for exploring data tables in Analytical Sciences. Here\, we present a course to illustrate the main benefits and drawbacks of PCA when applied to various types of analytical data\, including spectroscopy\, environmental assessment\, sensory experiments\, performance experiments\, and chromatography. Moreover\, the preprocessing of different types of data will also be addressed in the seminar as a prerequisite for exploring the data optimally. If PCA is the keystone of pattern recognition methods\, PLS is the keystone of multivariate calibration methods. This seminar will give a general overview of different multivariate calibration strategies (Multilinear Regression\, Principal Component Regression)\, and will focus on Partial Least Squares regression.\n– LinAl: Linear Algebra. The Foundation for chemometric modelling is Linear Algebra. Why do the algorithms work? Why are the models meaningful? A math-derived answer to these questions can be found using linear algebra. The seminar will focus on hands-on experience with some fundamental linear algebra concepts\, including rank\, determinant\, inverse\, pseudo inverse\, eigenvalues\, singular value decomposition\, orthogonality and basis sets. We will analyze a few real-life datasets\, but the purpose of the seminar is to be a proficient mechanic unravelling the black box of algorithms and models\, while other courses will teach you how to drive a car. \nImportant: Refer to the detailed calendar for additional information. The modules are not divisible. Therefore\, the entire week counts as a single course. \nPrevious knowledge needed: None \nSoftware needed: Feel free to work with Matlab\, Python or R\, or any other software that you consider (e.g. Unscrambler). The teachers will work with:\n– Matlab\n– PLS_Toolbox / SOLO. IMPORTANT: For the PLS_Toolbox / SOLO\, a fully functional demo will be available for the School. \nTeachers: José Manuel Amigo\, Beatriz Quintanilla\, Morten A. Rasmussen. See guest lecturers for further information. \nLearning outcomes\nIntended learning outcome for the students who complete the ISC-2026 complete course: \nKnowledge\n• Learn the basics of data analysis methods.\n• Learn to handle data and create proper datasets and libraries for further analysis\n• Learn critical thinking regarding Machine Learning\, Chemometrics and IA \nSkills\n• Develop their own data analysis protocols\n• Code basic algorithms and the resources available for data analysis\n• Apply the acquired knowledge to any problem related to their own research \nCompetences\n• Understand the structure of a vast number of data types and the issues derived from the data\n• Independent thinking for the solution of their problems\n• Interaction with other peers and teachers \nTarget Group\nThe course is specifically addressed to PhD students.\nAdditionally\, the course attracts a high number of BSc\, MSc\, postdoctoral researchers\, and professors.\nAnother relevant audience is Industry. The course receives students from 2 to 3 companies every year. \nRecommended Academic Qualifications\nNone specifically required. We start from basic topics and go all the way to a more advanced topics. \nResearch Area\nChemometrics\, machine learning\, spectroscopy\, artificial intelligence\, programming\, statistics \nTeaching and Learning Methods\nThe seminars of the International School of Chemometrics will comprise a mix of presentations from world-leading researchers\, combined with practical and theoretical exercises in data analytics software\, which will provide students with hands-on experience in applying the tools taught. The exercises are done under the supervision of the teachers.\nThe initial week of programming offers instruction in three different languages (MATLAB\, R and Python)\, and all the instruction in this part is based on e-learning. The remaining three weeks of the school are dedicated to physical on-site training. \nType of Assessment\nThe course is completed by attending and development during the practical exercises will be evaluated by interest of the student. \nLiterature\nPeer-reviewed papers provided during the course. \nCourse coordinator\nRasmus Bro\, Professor\, rb@food.ku.dk \nGuest Lecturers\n– Prof. Rasmus Bro\, University of Copenhagen. Main coordinator. Teacher in CHALLENGES (Multiway and GLUE).\n– Prof. José Amigo Rubio\, University of the Basque Country. Primary person responsible for day-to-day business operations throughout the entire School. Teacher in PROGRAMMING (MATLAB)\, BASICS and CHALLENGES (GLUE).\n– Assistant Prof. Beatriz Quintanilla\, University of Copenhagen. Primary person responsible for day-to-day business operations throughout the entire School. Teacher in BASICS and CHALLENGES (Multiway and GLUE).\n– Prof. Morten A. Rasmussen\, University of Copenhagen. Teacher in BASICS (LinAl).\n– Assoc. Prof. Asmund Rinnan\, University of Copenhagen. Teacher in INTERMEDIATE (VarSel).\n– Assoc. Prof. Agnieszka Smolinska\, Maastricht University. Teacher in INTERMEDIATE (DoE-ASCA).\n– Prof. Davide Ballabio\, University of Milano-Bicocca. Teacher in INTERMEDIATE (CLASS).\n– Prof. Anna de Juan\, University of Barcelona. Teacher in CHALLENGES (MCR).\n– Dr. Neal Galhaguer\, Eigenvector Research. Teacher in CHALLENGES (HYPER).\n– Dr. Carlos de Cos\, The Mathworks. Teacher in CHALLENGES (NonLin).\n– Assoc. Prof. Sergey Kucheryavskiy\, University of Aalbrog. Teacher in PROGRAMMING (R).\n– Dr. Anders Krogh Mortensen\, The AI Lab. Teacher in PROGRAMMING (Python).\n– Prof. Federico Marini\, University of Rome La Sapienza. Teacher in CHALLENGES (GLUE). \nDates\nPROGRAMMING: 13th April – 17th April\, 2026.\nBASICS: 20th April – 24th April\, 2026\nINTERMEDIATE: 25th April – 1st May\, 2026\nCHALLENGES: 4th May – 8th May\, 2026 \nDetailed calendar\nISC-2026 \nWeek 01 – Online PROGRAMMING\n13-april 14-april 15-April 16-april 17-april\nProgramming Programming Programming Programming Programming \nWeek 02 – BASIC\n20-april 21-april 22-april 23-april 24-april\nPCA LinAl PREPO REG REG \nWeek 03 – INTERMEDIATE\n25-april 26-april 27-april 28-april 01-may\nVARSEL VARSEL CLASS CLASS DoE – ASCA \nWeek 04 – CHALLENGES\n04-may 05-may 06-may 07-may 08-may\nMCR MCR NonLin NonLin GLUE – 1000M\nHYPER HYPER MULTIWAY MULTIWAY \nCourse location\nFrederiksberg Campus \nCourse fee\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3000 DKK\n• PhD student from foreign university: 3000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant type – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/international-school-of-chemometrics-basics/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260420
DTEND;VALUE=DATE:20260425
DTSTAMP:20260414T174327
CREATED:20260119T134442Z
LAST-MODIFIED:20260309T135237Z
UID:10001855-1776643200-1777075199@ddsa.dk
SUMMARY:Numerical Optimization
DESCRIPTION:Enrolment guidelines  \nThis is a toolbox course where 80% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved for PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nNumerical optimization is a key computer tool across various fields\, including image processing\, machine learning\, bioinformatics\, economics\, etc. It addresses diverse problems\, Maximum Likelihood or Maximum a Posteriori parameter estimation\, inverse kinematics in robotics and many optimization problems in imaging\, denoising\, segmentation\, reconstruction etc. for instance in medical imaging. \nThis course will equip PhD students with a set of numerical optimization techniques\, making it an excellent addition for those from various scientific backgrounds. It covers the fundamental theory and practical implementation of these methods\, emphasizing deep understanding\, mathematical derivation\, and programming best practices. Students will also be trained on practical examples from the research directions pursued in the IMAGE section. \nLearning outcomes \nKnowledge:\n1. Line search Gradient descent and Newton Method\, Trust Regions\, Gauss-Newton\, Levenberg-Marquardt\, simple constrained optimization including linear programming (simplex and interior point methods) etc. \nSkills:\n2. Ability to use numerical optimization solutions in practice\n3. Ability to use optimization toolboxes such as Python SciPy optimisation packages as well as others. \nCompetences:\n4. Identify practical situations where numerical optimisation is needed.\n5. Ability to formulate a problem as a numerical optimisation task.\n6. Ability to choose a suitable optimization method \nTarget Group\nPh.D. students in computer science\, mathematics\, chemistry\, economics and physics \nRecommended Academic Qualifications\nM.Sc in computer science\, mathematics\, chemistry\, economics and physics or equivalent. \nResearch Area\nComputer Science\, mathematics\, chemistry\, economics and physics. \nTeaching and Learning Method\n5 full days with morning lecture and afternoon exercises \nType of Assessment\nOne or two large take home assignments. \nLiterature\nNumerical Optimization\, J. Nocedal and S. J. Wright. Springer. Course Notes. \nCourse coordinator\nFrançois Lauze\, Associate Professor\, DIKU. \nGuest Lecturer\nBernhard Kerbl\, Assistant Professor\, DIKU \nDates\nBlock 4\, April 20-24\, 2026 \nExpected frequency\nOnce a year\, block 4 unless there is enough interest from students\, then we will run a new occurrence in the beginning of block 2. \nCourse location\nNorth Campus or Frederiksberg Campus \nRegistration\nRegistration with waiting list \nDeadline for registration\n4 weeks before course starts. If seats are available late registration might be accepted (with a cap on 30 participants) \nCourse fee\n• Participant fee: 0 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3000 DKK\n• PhD student from foreign university: 3000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/numerical-optimization/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260428
DTEND;VALUE=DATE:20260527
DTSTAMP:20260414T174327
CREATED:20260204T133452Z
LAST-MODIFIED:20260309T141351Z
UID:10001734-1777334400-1779839999@ddsa.dk
SUMMARY:Machine Learning for SCIENCE (MLS)
DESCRIPTION:Enrolment guidelines  \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nThe Machine Learning for SCIENCE (MLS) course introduces key analysis methods in Machine Learning. These methods allow investigations of scientific data from most fields\, including data from physical measurements\, questionnaires\, pictures\, internet searches\, satellites\, and biochemical analyses. We cover data cleaning (e.g. missing data\, denoising)\, feature extraction\, machine learning basics (labels\, variables\, parameter optimization\, overfitting\, cross-validation)\, key machine learning and image analysis methods based on both unsupervised and supervised learning\, and visualization. Method-wise\, we start at Linear Discriminant Analysis and end with Deep Learning.\nAt the end of the course\, the students must write a report with a suggestion for an analysis ideally performed on their own research data including a small implementation of a key concept. This report could form the basis for the Data Science Projects PhD course also offered by the Data Science Lab. \nLearning outcomes\nIntended learning outcome for the students who complete the course: \nKnowledge\n• Understand key machine learning concepts (e.g. parameter training\, overfitting).\n• Understand key machine learning methods (e.g. LDA\, supervised learning).\n• Understand key data analysis methods (e.g. feature extraction). \nSkills\n• Develop/adapt/extend a computer-based software method for analysis of relevant data. \nCompetences\n• Propose relevant analysis methods for scientific data science problems.\n• Consider cross-disciplinary data science methods in their research. \nTarget Group\nPhD students from all SCIENCE departments with an element of data science in their research project. \nRecommended Academic Qualifications\nWe use Python for the examples and exercises\, so a basic level of Python programming experience is needed.\nThe Python skills could come from the Python for SCIENCE PhD toolbox course. \nResearch Area\nAll SCIENCE research fields\, and secondarily other scientific fields with a data science element (e.g. health sciences). \nTeaching and Learning Methods\nThe course is composed of sessions combining lectures and exercises.\nFor each topic\, the students will get hands-on experience in applying\, modifying\, and programming analysis methods.\nThe programming examples will be implemented using Python in JupyterLab notebooks. \nType of Assessment\nThe students need to hand in their reports (10 days after the final course day) that must be approved. The students are allowed to work in 2-person groups. \nLiterature\nCourse lecture slides and exercises.\nWe will use data\, examples\, and other material from publicly available sources. \nCourse coordinator\nErik Dam\, Professor\, erikdam@di.ku.dk \nDates\n2026: Apr 28\, May 5\, May 12\, May 19\, May 26. \nCourse location\nPhysically on campus.\nTypically\, at Nørre Campus\, alternatively at Frederiksberg Campus. \nCourse fee\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3600 DKK\n• PhD student from foreign university: 3600 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3600 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3600 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 10.080 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/machine-learning-for-science-mls-2/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260504
DTEND;VALUE=DATE:20260509
DTSTAMP:20260414T174327
CREATED:20260205T121650Z
LAST-MODIFIED:20260309T141452Z
UID:10001717-1777852800-1778284799@ddsa.dk
SUMMARY:Estimating Causal Effects with Observational Data
DESCRIPTION:Enrolment guidelines  \nThis is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come\, first-served basis and according to the applicable rules. \nAnyone can apply for the course\, but if you are not a PhD student at a Danish university (except CBS)\, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline\, available seats will be allocated to applicants on the waiting list. \nAim and Content\nResearchers are often interested in investigating causal relationships\, i.e.\, if and how one variable affects another. While the analysis of causal relationships is ideally done using experimental data\, in several research areas (e.g.\, social sciences) experiments are often infeasible or suffer from important limitations. As a result\, most empirical studies in the social sciences and related research areas are based on observational (i.e.\, nonexperimental) data.\nParticipants in this course will learn state-of-the-art methods used for investigating causal relationships with observational data. Course participants will also learn how to evaluate and discuss the appropriateness of research designs (“identification strategies”) and empirical methods for analysing causal relationships\, and they will learn to choose the most appropriate research designs and empirical methods for a specific research question. This will help participants obtain more credible and reliable results in their own research.\nTopics taught in this course include causal directed acyclic graphs (DAGs)\, methods based on ‘selection-on-observables’\, methods based on instrumental variables\, synthetic control methods\, regression discontinuity designs\, difference-in-differences\, methods for panel data with staggered treatment\, and causal machine learning methods. The course participants will learn the theoretical background and underlying assumptions of these methods as well as how to apply them in real-world analyses. \nLearning outcomes\nIntended learning outcomes for the students who complete the course: \nKnowledge\n• Understand causal DAGs.\n• Describe methods for causal inference with observational data\, including\n• ‘Selection-on-observables’\n• Instrumental variables\n• Synthetic control\n• Regression discontinuity designs\n• Difference-in-differences\n• Methods for panel data with staggered treatment\n• Causal machine learning\n• Describe the assumptions that need to be fulfilled if the methods listed above should give reliable estimates of causal effects. \nSkills\n• Construct and interpret causal DAGs and use them to identify causal effects using the DAGitty software.\n• Apply methods for causal inference with observational data using (statistical) software such as R\, Stata\, or Python.\n• Assess to which extent assumptions that are required by different causal inference methods with observational data are fulfilled in specific real-world applications. \nCompetences\n• Choose research designs and methods that are appropriate for causal inference with observational data in their research area.\n• Critically evaluate the appropriateness of research designs (“identification strategies”) and methods for answering causal questions with observational data in their research area (this refers to their own research\, e.g.\, when discussing strength and weaknesses of causal analyses in their own papers\, as well as to the research done by others\, e.g.\, when reviewing manuscripts or assessing the reliability of causal analyses). \nTarget Group\nPh.D. students at SCIENCE\, SUND\, SAMF\, and other faculties or universities\, who aim to investigate causal questions with observational data (e.g.\, economists\, other social scientists\, nutritionists\, epidemiologists\, other health/veterinary scientists\, etc.). \nRecommended Academic Qualifications\nThe students should have basic knowledge in statistics (e.g.\, hypothesis tests\, ordinary least-squares (OLS)\, etc.) obtained\, e.g.\, in the statistics variant of the PhD course “Fundamentals of the PhD education at SCIENCE – module 2” or a similar course. \nResearch Area\nAll research areas that apply statistical methods to answer causal research questions with observational data\, including economics\, other social sciences\, nutritional sciences\, epidemiology\, other health/veterinary sciences\, etc. \nTeaching and Learning Methods\nThe course participants are encouraged to read some of the course material before the course starts to be well prepared. The course consists of a combination of lectures and practical exercises. The participants will construct and interpret causal DAGs and they will learn to implement various methods for estimating causal effects. While the teachers will use the R software to present solutions to these exercises\, the participants are free to use other software (e.g.\, Stata or Python). The practical exercises also include group discussions\, e.g.\, about the appropriateness of research designs (“identification strategies”) and empirical methods. The course participants can choose to write a short report (5-10 pages)\, in which they apply at least one of the methods taught in the course to simulated or real-world observational data\, e.g.\, as a part of their PhD project. Reproducibility of the empirical analysis will play a key role in the lectures\, the practical exercises\, and in the ‘short report’ (exam). \nType of Assessment\nThe participants get this course approved with 2.5 ECTS if they attend the lectures\, do the practical exercises\, and pass a multiple-choice test given at the end of course.\nThe participants get this course approved with 5 ECTS if they additionally write and submit a short report (see above) that is positively assessed by the teachers. This short reported has to be submitted to the course coordinator no later than 3 months after the end of the course. \nLiterature\n• Angrist\, J.D. and Pischke\, J.-S. (2009)\, Mostly Harmless Econometrics\, Princeton University Press.\n• Angrist\, J. D. and Pischke\, J. S. (2014). Mastering ‘Metrics: The Path from Cause to Effect. Princeton University Press.\n• Bellemare\, M.F.\, Bloem\, J.R. and Wexler\, N. (2024): The Paper of How: Estimating Treatment Effects Using the Front-Door Criterion. Oxford Bulletin of Economics and Statistics 86: 951-993. https://doi.org/10.1111/obes.12598\n• Didelez\, V. (2025): Causal Reasoning and Inference in Epidemiology. In Ahrens\, W. and Pigeot\, I: Handbook of Epidemiology\, Springer\, New York. https://doi.org/10.1007/978-1-4614-6625-3_74-1\n• Digitale\, J.C.\, Martin\, J.N. and Glymour\, M.M. (2022). Tutorial on directed acyclic graphs. Journal of Clinical Epidemiology\, 142\, pp.264-267.\n• Henningsen\, A.\, Low\, G.\, Wuepper\, D.\, Dalhaus\, T.\, Storm\, H.\, Belay\, D. and Hirsch\, S (2025): Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists. arXiv preprint. https://doi.org/10.48550/arXiv.2508.02310.\n• Hernán\, M.A. (2018). The C-word: scientific euphemisms do not improve causal inference from observational data. American journal of public health\, 108(5)\, pp.616-619.\n• Hernán & Robins (2020): Causal Inference: What If? Chapman & Hall/CRC\, Boca Raton (particularly chapters 1\, 2\, 3\, 4\, 6\, 7\, 8 & 16)\, https://miguelhernan.org/whatifbook\n• Huber\, M. (2023): Causal analysis: Impact evaluation and Causal Machine Learning with applications in R. MIT Press.\n• Huber\, M. (2025): Impact Evaluation in Firms and Organizations: With Applications in R and Python. MIT Press.\n• Morgan\, S.L. and Winship\, C. (2014)\, Counterfactuals and Causal Inference: Methods and Principles for Social Research\, 2nd ed. Cambridge University Press.\n• Tennant\, P.W.\, Murray\, E.J.\, Arnold\, K.F.\, Berrie\, L.\, Fox\, M.P.\, Gadd\, S.C.\, Harrison\, W.J.\, Keeble\, C.\, Ranker\, L.R.\, Textor\, J. and Tomova\, G.D. (2021). Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International journal of epidemiology\, 50(2)\, pp.620-632.\n• Textor\, J. (2015)\, Drawing and analyzing causal DAGs with DAGitty. arXiv preprint arXiv:1508.04633 \nCourse coordinator\nArne Henningsen\, Associate Professor\, arne@ifro.ku.dk \nTeachers\nArne Henningsen\, Associate Professor\, arne@ifro.ku.dk\nBo Markussen\, bomar@math.ku.dk\nChristine Winther Bang\, cwb@math.ku.dk \nGuest Lecturers\nWhen we taught the course in 2025\, we invited two renowned experts to give guest lectures on two of the methods covered in this course\, respectively. As this worked very well\, we plan to include the same or similar guest lectures in the 2026 course as well. \nDates\n4-8 May 2026 – 9-17 all days. \nExpected frequency\nOnce per year in teaching block 4. \nCourse location\nUCPH Campus \nCourse fee\n• Participant fee: 0 DKK\n• PhD student enrolled at SCIENCE: 0 DKK\n• PhD student from Danish PhD school Open market: 0 DKK\n• PhD student from Danish PhD school not Open market: 3000 DKK\n• PhD student from foreign university: 3000 DKK\n• Master’s student from Danish university: 0 DKK\n• Master’s student from foreign university: 3000 DKK\n• Non-PhD student employed at a university (e.g.\, postdocs): 3000 DKK\n• Non-PhD student not employed at a university (e.g.\, from a private company): 8400 DKK \nCancellation policy\n• Cancellations made up to two weeks before the course starts are free of charge.\n• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000\n• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000\n• No-show will result in a fee of DKK 5.000\n• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 \nCourse fee and participant fee\nPhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.\nIn addition to the course fee\, there might also be a participant fee.\nIf the course has a participant fee\, this will apply to all participants regardless of participant\ntype – and in addition to the course fee. \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/estimating-causal-effects-with-observational-data/
CATEGORIES:PhD Course
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