Discovering Context Specific Causal Relationships

TL;DR

This paper introduces TCC, a decision tree-based method that efficiently discovers causes and their specific contexts simultaneously from observational data, advancing personalized decision-making applications.

Contribution

The paper presents TCC, a novel decision tree approach for discovering context-specific causal relationships directly from observational data, addressing a gap in existing methods.

Findings

TCC effectively discovers context-specific causal rules in synthetic data.

TCC successfully identifies causes and contexts in real-world datasets.

The method outperforms existing approaches in efficiency and accuracy.

Abstract

With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships. Most existing methods, however, assume a known cause (e.g. a new drug) and focus on identifying from data the contexts of heterogeneous effects of the cause (e.g. patient groups with different responses to the new drug). There is no approach to efficiently detecting directly from observational data context specific causal relationships, i.e. discovering the causes and their contexts simultaneously. In this paper, by taking the advantages of highly efficient decision tree induction and the well established causal inference framework, we propose the Tree based Context Causal rule discovery (TCC) method, for efficient exploration of context specific causal relationships from data. Experiments with both synthetic and real world data sets show that TCC can effectively discover context specific causal rules from the data.