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62629-01 - Blockkurs: Hands on Causal Inference With R 1 KP

Semester Herbstsemester 2022
Angebotsmuster unregelmässig
Dozierende Tracy Glass (tracy.glass@unibas.ch)
Giusi Moffa (giusi.moffa@unibas.ch, BeurteilerIn)
Inhalt Formulation of causal questions, definition of causal effects in the potential outcome framework and causal identifiability assumptions.

Reasoning about causality with directed acyclic graphs, an effective tool to describe the causal assumptions underlying a study, identify valid covariate adjustment sets and uncover potential pitfalls in study design and analysis, especially related to confounding and collider bias.

Implementation with R of the most common analytical methods to control for confounding and estimate causal contrasts/effects for point exposures from observational data, including stratification, outcome regression, propensity score matching and inverse probability weighting.

Methods for causal inference in longitudinal settings with time-varying exposures and time-varying confounding.
Lernziele The focus of the course is on the practical software implementation of statistical analyses for causal inference. The course is intended to help students develop their ability to:

- formulate causal questions and define causal estimands addressing a specific research question.

- use DAGs to describe causal assumptions and guide the choice of suitable statistical analysis strategies.

- understand the assumptions underlying the estimation of causal contrasts/effects of interest.

- choose appropriate methods to estimate causal contrasts/effects from real data, and implement them using the `R` statistical software.
Bemerkungen Assistant: Enrico Giudice


Teilnahmebedingungen Prior knowledge in statistics, especially statistical inference and regression modelling, with some experience in implementing statistical analyses, preferably with R/RStudio which we will use for all practical examples.

Students should have access to a laptop with R/Rstudio installed.
Unterrichtssprache Englisch
Einsatz digitaler Medien kein spezifischer Einsatz
HörerInnen willkommen


Intervall Block
Datum 12.09.2022 – 15.09.2022
Zeit Siehe Detailangaben
Datum Zeit Raum
Montag 12.09.2022 09.15-13.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Dienstag 13.09.2022 09.15-13.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Mittwoch 14.09.2022 09.15-13.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Donnerstag 15.09.2022 09.15-13.00 Uhr Spiegelgasse 5, Seminarraum 05.002
Module Modul: Advances in Epidemiology, Statistics and Global & Public Health (Masterstudium: Epidemiologie)
Modul: Angewandte Mathematik (Bachelorstudium: Mathematik)
Modul: Applications and Related Topics (Bachelor Studienfach Computer Science)
Modul: Applications and Related Topics (Bachelorstudium: Computer Science)
Modul: Electives in Data Science (Masterstudium: Data Science)
Modul: Vertiefung Mathematik (Bachelorstudium: Computational Sciences)
Leistungsüberprüfung Lehrveranst.-begleitend
An-/Abmeldung zur Leistungsüberprüfung An-/Abmelden: Belegen resp. Stornieren der Belegung via MOnA
Wiederholungsprüfung keine Wiederholungsprüfung
Skala Pass / Fail
Wiederholtes Belegen beliebig wiederholbar
Zuständige Fakultät Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch
Anbietende Organisationseinheit Fachbereich Mathematik