But What If? A Brief Foray Into Causal Inference скачать в хорошем качестве

But What If? A Brief Foray Into Causal Inference

In experiments that aim to determine whether a treatment has an effect or not, randomised controlled trials (RCTs) are considered a gold standard. When RCTs are not possible due to practical or ethical constraints, observational data is often relied on instead. However, observational data is often wrought with problems such as confounding, which motivates the use of various causal inference methods. In this seminar, we begin by introducing and defining causal effects using Rubin’s potential outcomes framework. We then compare RCTs and observational data, and explore three causal inference methods: G-computation, Inverse Probability of Treatment Weighting, and Targeted Maximum Likelihood Estimation. We demonstrate how these methods address confounding, and also discuss their limitations.