Revisiting Causality Inference in Memory-less Transition Networks

TL;DR

This paper introduces CICT, a novel method for inferring causal relationships from one-step transition data using distribution zone characteristics and machine learning, demonstrating high accuracy in healthcare transition networks.

Contribution

The paper presents a new causality inference method for memory-less transition data, leveraging distribution zones and machine learning, filling a gap in existing methods that require longer time series.

Findings

High accuracy in inferring causal transitions

Effective in determining causality direction

Applicable to healthcare diagnosis transitions

Abstract

Several methods exist to infer causal networks from massive volumes of observational data. However, almost all existing methods require a considerable length of time series data to capture cause and effect relationships. In contrast, memory-less transition networks or Markov Chain data, which refers to one-step transitions to and from an event, have not been explored for causality inference even though such data is widely available. We find that causal network can be inferred from characteristics of four unique distribution zones around each event. We call this Composition of Transitions and show that cause, effect, and random events exhibit different behavior in their compositions. We applied machine learning models to learn these different behaviors and to infer causality. We name this new method Causality Inference using Composition of Transitions (CICT). To evaluate CICT, we used an administrative inpatient healthcare dataset to set up a network of patients transitions between different diagnoses. We show that CICT is highly accurate in inferring whether the transition between a pair of events is causal or random and performs well in identifying the direction of causality in a bi-directional association.