🇬🇧 Causal Inference Methods for Real-world data

Abstract

Estimating causal effects in non-experimental data is a key aim of applied health and social science research. Unfortunately, it is also notoriously difficult.

Contemporary causal inference methods, including directed acyclic graphs, promise to revolutionise the analysis and interpretation
non-experimental data, not least by making our ambitions and assumptions far more explicit. In the health sciences, these methods are rapidly gaining popularity, but they are still yet to be adopted as widely as other entrenched
methods.

This talk offers a simple introduction to the new
science of causal inference, with a particular focus on directed acyclic graphs.