Causal Inference (SL_114)
About Study Course
Objective
The objective of this course is to give students the understanding of the distinction between statistical models and causal models and knowledge of the methodology to assess identifiability of causal effects for a particular study, as well as skills to estimate causal parameters using some specific analysis tools.
The software package R will be used for computer practical classes, where mainly simulation methods are used to explore the validity of alternative methods. Also, several specialized R packages for causal inference will be introduced.
Prerequisites
• Familiarity with probability theory and mathematical statistics.
• Basic knowledge in R software.
• Basic knowledge of linear models and statistical estimation techniques.
Learning outcomes
The students will:
• compare the distinction between association models and causal models; the problem of confounding and the idea of adjustment and/or standardization to control for confounding.
• state terminology and properties of Directed Acyclic Graphs to describe and assess causal association structures in data.
• list special methods for estimation of causal effects: propensity score matching, inverse probability weighting, instrumental variables estimation.
• explain the essence of the problem of causal mediation, differention between direct and indirect effects.
The students who have completed the course, will be able to:
• decide, whether a study would lead to estimates with immediate causal interpretation.
• sketch a causal graph (a DAG) to understand and discuss identifiability of causal effects of interest.
• select an appropriate set of covariates for adjustment in regression analysis.
• independently use specialized tools (and corresponding R packages) for causal inference: propensity score matching, inverse probability weighting, instrumental variables estimation.
• communicate and present the findings in writing and oral of causal interpretation of the results of data analysis.
• The students will be competent in understanding and critical assessment of the published research that uses causal statements and/or causal inference methods for data analysis.
• The students will be competent in causal reasoning based on a study design and available data in an interdisciplinary research team.
• In particular, a student who has successfully passed the course, is able to assess (and explain), which of the following is valid in the particular study:
a) the causal effect of interest is estimable by standard modelling tools (with adjustment for confounders);
b) the causal effect of interest is estimable with specific methodology for causal inference;
c) the causal effect of interest cannot be identified;
In cases a) and b) the student will be competent to conduct the analysis, and disseminate new knowledge in health-related studies.
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Biostatistics, MFBS | 2 | Master’s | Required | Māris Munkevics | |
| Biostatistics, MFBSeng | 2 | Master’s | Required |
