Statistical Methods for Causal Inference

Statistical methods for improving causal analyses

Aims and Objectives

The aim of this programme is to develop methods for causal inference that are robust to missing data and can investigate change over time, in order to draw unbiased conclusions about realistic problems, using complex observational data.

1. Develop methods to minimise bias due to missing data

2. Develop methods to model complex exposures and outcomes

3. Develop IV methods to examine causal influences of multiple exposures

4. Integrate evidence to improve causal models


Programme Plan

Part 1 of this programme will develop methods to use internal and external information to infer the missing data structure, to inform all types of causal analyses. We will then focus on methods to maximise the robustness of IV methods to different types of missing data. We will pay particular attention to two cases: two-sample IV (using individual or summary data), and the investigation of disease prognosis. Part 2 will extend current methods for modelling trajectories and variability of exposures and outcomes. We will then focus on overcoming some of the current limitations of IV methods, by using structural equation modelling (SEM) and multivariable IV to examine impacts of time-varying exposures. Finally, we will maximise the use of all research data by extending methods to combine and use external information to inform causal models and sensitivity analyses.

Reseach Highlights

Professor Kate Tilling


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