Statistical Methods for Causal Inference

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Working out which treatments or interventions are effective, using only observational data, needs increasingly sophisticated statistical methods. We aim to develop methods to enable researchers to estimate the effects of interventions as accurately as possible.We will develop models to help identify those who would benefit most from a given intervention, so enabling better targeting of treatments. Most measures in observational data are made with some error (e.g. blood pressure varies throughout the day) and we will extend current methods to reduce the effect this has on causal estimates. We will also develop ways to use current methods based on mendelian randomization (MR) to improve analyses of non-genetic exposures, such as examining the benefits of cycling to work. Current methods tend to focus on one intervention, and we will extend these to examine the long-term effects, for example to assess the impact of remaining heavier than average throughout adolescence and adulthood vs losing weight during adulthood. Finally, we will improve methods for combining evidence from several different study types to answer the same question, by developing a triangulation framework.