  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.
