Front Door Criterion: Adjustment Formula

In course

Description

This lecture delves into the concept of the front door criterion, explaining the conditions required for a valid adjustment set in causal inference. The instructor demonstrates how to derive the front door adjustment formula step by step, emphasizing the importance of using observable variables. Through a detailed example involving confounding variables and causal effects, the lecture illustrates how to apply the front door criterion to analyze the impact of attending a university on marital success. By combining the backdoor criterion with the front door criterion, the instructor showcases a comprehensive approach to causal inference, highlighting the significance of proper adjustment sets in drawing accurate causal conclusions.

Official source

https://mediaspace.epfl.ch/media/0_3oojcsae

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In the philosophy of science, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development of a causal model. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for. They can allow some questions to be answered from existing observational data without the need for an interventional study such as a randomized controlled trial.

Rubin causal model

The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes, named after Donald Rubin. The name "Rubin causal model" was first coined by Paul W. Holland. The potential outcomes framework was first proposed by Jerzy Neyman in his 1923 Master's thesis, though he discussed it only in the context of completely randomized experiments.

Latent and observable variables

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