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| Semester | fall semester 2021 |
| Course frequency | Irregular |
| Lecturers |
Tracy Glass (tracy.glass@unibas.ch)
Giusi Moffa (giusi.moffa@unibas.ch, Assessor) |
| Content | 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. |
| Learning objectives | 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. |
| Admission requirements | 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. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| Block | See individual dates | ||
| Date | Time | Room |
|---|---|---|
| Monday 17.01.2022 | 09.15-13.00 | - Online Präsenz -, -- |
| Tuesday 18.01.2022 | 09.15-13.00 | - Online Präsenz -, -- |
| Wednesday 19.01.2022 | 09.15-13.00 | - Online Präsenz -, -- |
| Thursday 20.01.2022 | 09.15-13.00 | - Online Präsenz -, -- |
| Modules |
Modul: Applications and Related Topics (Bachelor's degree subject: Computer Science) Module: Advances in Epidemiology, Statistics and Global & Public Health (Master's Studies: Epidemiology) Module: Applications and Related Topics (Bachelor's Studies: Computer Science) Module: Applied Mathematics (Bachelor's Studies: Mathematics) |
| Assessment format | continuous assessment |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Science, studiendekanat-philnat@unibas.ch |
| Offered by | Fachbereich Mathematik |