Causal inference is a central aim of many empirical investigations, and arguably most studies in the fields of medicine, epidemiology and public health. However, traditionally, the role of statistics is often relegated to quantifying the extent to which chance could explain the results, whilst concerns over systematic biases due to the non-ideal nature of the data are relegated to their qualitative discussion. The field known as causal inference has changed this state of affairs, setting causal questions within a coherent framework which facilitates explicit statement of all the assumptions underlying a given analysis, in many settings developing novel, flexible analysis methods, and allowing extensive exploration of potential biases.
This course will discuss the current state of the art with respect to these issues, while retaining a practical focus. The potential outcomes framework, causal diagrams, standardization, propensity scores, inverse probability weighting, instrumental variables, marginal structural models, causal mediation analysis and examples of sensitivity analysis will be discussed. Participants will acquire awareness of the common threads across these new methods and competence in applying them in simple settings.
You can apply until:
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You can choose to do this programme part-time or full-time.Full-time
The topics covered will be:
This programme requires students to demonstrate proficiency in English.Take IELTS test
Participants will be expected to be numerate epidemiologists, or applied statisticians with an interest in epidemiology and clinical trials. An MSc in Epidemiology or Medical Statistics, or previous attendance to the Advanced Course in Epidemiological Analysis, would be an advantage.
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