Addressing Causal Questions using Real World Data: an introduction


15/05/2023 - 18/05/2023

Organised by:

University College London


Prof Bianca De Stavola, Dr Eduardo Fe, Dr Ellie Iob and Andrea Aparicio-Castro


Entry (no or almost no prior knowledge)


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Venue: Online


RADIANCE courses are targeted to the broad community of UK data scientists working in public health. They include epidemiologists, clinicians, data engineers/informaticians, statisticians, as well as quantitative researchers from other disciplines (e.g. psychology, social sciences, health economics).

They may be from academia, charities, government departments and non-profitable organisations.

Course description

This introductory course is for anyone wishing to understand how causal questions can be investigated using real world data (RWD), that is data on the everyday experiences of individuals that are collected through surveys, cohort studies, administrative and clinical databases or accrued for reasons other than research. These data are observational, as opposed to experimental. Because of this, using them to address causal questions raises many concerns and difficulties. In this course we will describe the main sources of bias affecting RWD and possible strategies to deal with them.

The course will start by distinguishing between different types of studies (e.g., RCTs, cross-sectional and longitudinal) and data sources (e.g., research-based, administrative databases). It will then describe the sources of bias that are likely to affect observational data, in particular those arising from the non-randomized allocation of exposures (denoted confounding bias in epidemiology and selection bias in the social sciences), from missing participation (including missing data), and from measurement errors. We will then introduce two main design-based approaches to attempt dealing with (some of) these biases: the framework of target trial emulation and the exploitation of natural experiments.



Website and registration:




Quantitative Data Handling and Data Analysis

Related publications and presentations:

Quantitative Data Handling and Data Analysis

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