Statistical Methods for Causal Inference
Statistical methods for improving causal analyses
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 model heterogeneity of causal effects
2. Develop methods to model measurement error and its impact on causal analyses
3. Develop methods to address complex causal and lifecourse hypotheses
4. Develop methods fordetecting and mitigating selection bias
5. Triangulation methods to quantitatively combine Mendelian Randomisation studies and randomised control trials
Reseach Highlights
Instrumental variables (IV)
We developed a new F-test statistic for determining whether the IV estimator in linear models suffers from weak instrument problems where there are multiple treatments that are potentially confounded. The problem here is that the instruments have to predict the multiple treatments jointly. The new conditional F-test statistic is shown to have similar properties to the standard F-test for the one-treatment model, and standard weak-instrument critical values can be used. It has been included in the user written software ivreg2 in Stata.
Key publication: Sanderson, E, Windmeijer F (2015). A Weak Instrument F-test in Linear IV Models with Multiple Endogenous Variables. Journal of Econometrics. Epub ahead of print:
Modelling change over the lifecourse
We developed a new method to identify which of a small set of hypothesized models explains most of the observed outcome variation, and showed that our approach identified the correct model with high probability in moderately sized samples, but with lower probability for hypotheses involving highly correlated exposures. Identifying a single, simple hypothesis that represents the specified knowledge of the life course association allows more precise definition of the causal effect of interest.
Key publication: Smith AD, Heron J, Mishra G, Gilthorpe MS, Ben-Shlomo Y, Tilling K (2015).
Model Selection of the Effect of Binary Exposures over the Life Course. Epidemiology. Epub ahead of print: PMID: 26172863
