Revisiting Causality Inference in Memory-less Transition Networks
This work addresses the challenge of causality inference for researchers and practitioners in fields like healthcare, where short, memory-less data is common, offering a novel approach but is incremental as it builds on existing causality methods.
The paper tackled the problem of inferring causal networks from memory-less transition data, which is widely available but previously unexplored for causality inference, and introduced the CICT method that uses machine learning to identify causal relationships based on distribution zones around events, achieving high accuracy in distinguishing causal from random transitions and determining causality direction.
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.