Causal Inference in Observational Data

TL;DR

This paper introduces a causal rule mining framework based on the Rubin-Neyman model to identify effective intervention combinations in observational health data, addressing biases and uncovering complex treatment effects.

Contribution

It proposes a novel causal rule mining method with the concept of closed intervention sets, tailored for observational health data analysis, improving causal inference over association rule mining.

Findings

Identified causal intervention patterns in EHR data from Mayo Clinic.

Explained controversial effects of cholesterol drugs on T2DM.

Demonstrated the framework's ability to correct biases in observational data.

Abstract

Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial pattern exploration problem. Association rule mining is a popular tool for such problems, but the requirement of health care for finding causal, rather than associative, patterns renders association rule mining unsuitable. To address this issue, we propose a novel framework based on the Rubin-Neyman causal model for extracting causal rules from observational data, correcting for a number of common biases. Specifically, given a set of interventions and a set of items that define subpopulations (e.g., diseases), we wish to find all subpopulations in which effective intervention combinations exist and in each such subpopulation, we wish to find all intervention combinations such that dropping any intervention from this combination will reduce the efficacy of the treatment. A key aspect of our framework is the concept of closed intervention sets which extend the concept of quantifying the effect of a single intervention to a set of concurrent interventions. We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).