Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs

TL;DR

This paper introduces a reparameterization of directed acyclic graphs using structural nested models to improve the estimation of effects from sequential treatments, addressing limitations of traditional parameterizations.

Contribution

It proposes a novel reparameterization approach with structural nested models to better estimate effects of sequential treatments in causal graphs.

Findings

Reparameterization avoids deficiencies of standard methods.

Structural nested models improve effect estimation accuracy.

Method enhances causal inference in sequential treatment settings.

Abstract

The standard way to parameterize the distributions represented by a directed acyclic graph is to insert a parametric family for the conditional distribution of each random variable given its parents. We show that when one's goal is to test for or estimate an effect of a sequentially applied treatment, this natural parameterization has serious deficiencies. By reparameterizing the graph using structural nested models, these deficiencies can be avoided.