Unifying Causal Models with Trek Rules

TL;DR

This paper introduces a method to unify causal models from fragmented datasets by leveraging additional information in marginal data, reducing the ambiguity in causal explanations and sometimes identifying a unique model.

Contribution

It demonstrates how marginal datasets contain extra information that can narrow down causal models, improving upon existing methods that produce many alternatives.

Findings

Marginal datasets can provide additional causal information.

The method can sometimes identify a unique causal model.

It reduces the number of plausible causal explanations.

Abstract

In many scientific contexts, different investigators experiment with or observe different variables with data from a domain in which the distinct variable sets might well be related. This sort of fragmentation sometimes occurs in molecular biology, whether in studies of RNA expression or studies of protein interaction, and it is common in the social sciences. Models are built on the diverse data sets, but combining them can provide a more unified account of the causal processes in the domain. On the other hand, this problem is made challenging by the fact that a variable in one data set may influence variables in another although neither data set contains all of the variables involved. Several authors have proposed using conditional independence properties of fragmentary (marginal) data collections to form unified causal explanations when it is assumed that the data have a common causal explanation but cannot be merged to form a unified dataset. These methods typically return a large number of alternative causal models. The first part of the thesis shows that marginal datasets contain extra information that can be used to reduce the number of possible models, in some cases yielding a unique model.