But What If? A Brief Foray Into Causal Inference

Overview

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.

The talk will be about 40 minutes long and following by discussion.

Date: Thursday 27 February 2025

Time: 2.30pm - 3.30pm

Speaker: Nickson Ning, Statistical Consultant, UNSW Stats Central

Delivery Mode: Hybrid

Location/Meeting link: TBA

FAQs

How can I contact the organiser with any questions?

Please send us an email at: stats.central@unsw.edu.au