Discovering Context Specific Causal Relationships
This addresses the need for personalized decision-making in fields like medicine and recommendations by enabling efficient detection of heterogeneous causal effects, though it is incremental as it builds on existing causal inference and decision tree methods.
The paper tackles the problem of discovering context-specific causal relationships from observational data, proposing the TCC method which efficiently identifies both causes and their contexts simultaneously, with experiments on synthetic and real-world datasets showing effective discovery.
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.