Causal Decision Trees

TL;DR

This paper introduces a causal decision tree method that interprets nodes causally, enabling scalable and automated causal discovery in large datasets without the need for experimental data.

Contribution

It develops a novel causal decision tree framework that integrates causal inference principles with classification trees, using statistical tests for causal interpretation.

Findings

Effective in large datasets

Provides causal interpretations at tree nodes

Avoids need for experimental data

Abstract

Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases. Causal relationships can also be found using some well designed observational studies, but they require domain experts' knowledge and the process is normally time consuming. Hence there is a need for scalable and automated methods for causal relationship exploration in data. Classification methods are fast and they could be practical substitutes for finding causal signals in data. However, classification methods are not designed for causal discovery and a classification method may find false causal signals and miss the true ones. In this paper, we develop a causal decision tree where nodes have causal interpretations. Our method follows a well established causal inference framework and makes use of a classic statistical test. The method is practical for finding causal signals in large data sets.