CourseSpecifications
Valid as from the academic year 2019-2020

Causality and Missing Data (C002881)

Course size
Credits 5.0(nominal values; actual values may depend on programme)
Study time 150 h
Contact hrs

62.5h

Course offerings in academic year 2024-2025

Lecturers in academic year 2024-2025
Offered in the following programmes in 2024-2025

crdts

offering

Teaching languages
EnglishKeywords
Causal inference, Experimental studies, Missing data, Observational studies, Selection bias
Position of the course
To enable the master to• recognize diverse forms of bias, due to missing data, confounding and selection bias, in• statistical analyses• avoid such biases through study design• correct for such biases through statistical data analysis.
This course provides an excellent training in scientific thinking.
This course builds on `Analysis of continuous data’Contents
This course offers a thorough investigation of statistical methods for causal inference fromexperimental and observational data. This methodology has wide applications in epidemiology,
clinical studies, public health, agriculture, sociology, pedagogy, demography, economics...
In the first part of this course, a general causal theory will be introduced which will enable asystematic study of different important types of bias in the statistical analysis of experimental
and observational data. Epidemiological concepts such as direct causal effect, indirect causaleffect, confounding, selection bias and intermediate variables will be formally defined using
potential outcomes and made intuitive using causal diagrams. Biases due to missing data inempirical studies and due to inappropriate adjustment for intermediate variables and timedependent confounders will be studied as special cases of this general causal theory rather
than as separate cases.
Next, several techniqies for confounding adjustment in point treatment studies are discussed:
standard regression adjustment, standardisation and propensity score based methods that use
subclassification, matching, regression or inverse weighting, as well as the use of machinelearning.
The third part of the course focuses on mediation analysis. It discusses controlled directeffects, natural direct and indirect effects, the mediation formula, natural effect models andfinally techniques for handling time-varying confounding: inverse weighting and G-estimation.
In the fourth part of this course, we will introduce marginal structural models for the analysis oftime-dependent exposures in the presence of time-dependent confounders.
Finally, the problem of incomplete data will be studied in more detail. The classical missing
data taxonomy (missing (completely) at random, missing not at random) will be described andits plausibility evaluated in different settings. Imputation methods are discussed in detail; theEM-algorithm and inverse probability weighted estimators are only briefly mentioned. The
importance of sensitivity analyses will be stressed.
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If time allows, instrumental variable methods will be discussed and illustrated in the context ofimperfect randomized studies (e.g. randomized clinical trials with noncompliance).
The different concepts and techniques will be illustrated using real data sets fromepidemiology, sociology and economics.
Initial competences
Having successfully completed the course `Analysis of continuous data’ or having acquired
otherwise the corresponding competences.
Final competences
1 Make the fundamental distinction between association analysis and causal analysis.
2 Recognize the impact of missing data on a statistical analysis.
3 Correct for selection bias and information loss due to missing data in standard statistical
1 analyses.
4 Understand the assumptions underlying different standard methods to correct for selection
1 bias and information loss due to missing data in statistical analyses.
5 Correct for measured (time-dependent) confounders in statistical analyses.
6 Understand the assumptions underlying different standard methods to correct for measured
1 (time-dependent) confounders in statistical analyses.
7 Correct for unmeasured confounders in statistical analyses, when an instrumental variable
1 has been measured.
8 Detect and model interaction terms.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Group work, Lecture, Seminar: coached exercises, Seminar: practical pc room classes
Extra information on the teaching methods
Exercises: written exercises and PC-labs using SAS and R.
Study material
NoneReferences
Little RJ and Rubin DB (1987). Statistical Analysis with Missing Data. New York: Wiley.
Pearl J (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
van der Laan MJ and Robins JM (2002). Unified Methods for Censored Longitudinal Data andCausality. Springer-Verlag: New-York.
Course content-related study coaching
The students will frequently exercise the concepts and methods explained during the lectures,by analyzing realistic data sets during the practical sessions, where students will be closely
supervised, and while making their project work. Besides the questions that students can ask
before, during or after each lecture, there are several possibilities for asking questions:interactive support via Ufora (forum) and, in case of larger numbers of questions, personalcoaching after electronic appointment.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Oral examination, Open book examination, Written examination with open questions
Examination methods in case of periodic assessment during the second examination period
Oral examination, Open book examination, Written examination with open questions
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible
Extra information on the examination methods
Theory: oral (open book)
Exercises: written (open book)(Approved)

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Project: written reporting
The entire exam assesses the student’s insight into the basic principles of causal inference andincomplete data analysis and his/her ability to actively apply the statistical methods in thecourse. The exam will consist almost entirely of exercises (e.g. questions to examine whether
the student is able to critically read scientific papers that involve causal questions or suffer frommissing data, practical insight may also be tested via interpretation of given software-output).
Calculation of the examination markTheory: periodic
Exercises: periodic and permanent (project work)
One group project (written reporting) will be assigned.
Calculation of the total score: exam 80%, project 20%. A second examination chance for theproject is possible. Non-participation to at least one of the project works implies a maximum
score (exam + project) of at most 7/20, regardless of the score obtained on the final exam.

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