Causal Relationship Network of Risk Factors Impacting Workday Loss in
  Underground Coal Mines
Shangsi Ren Cameron A. Beeche Zhiyi Shi Maria Acevedo Garcia Katherine Zychowski Shuguang Leng Pedram Roghanchi Jiantao Pu
This study aims to establish the causal relationship network between various
factors leading to workday loss in underground coal mines using a novel causal
artificial intelligence (AI) method. The analysis utilizes data obtained from
the National Institute for Occupational Safety and Health (NIOSH). A total of
101,010 injury records from 3,982 unique underground coal mines spanning the
years from 1990 to 2020 were extracted from the NIOSH database. Causal
relationships were analyzed and visualized using a novel causal AI method
called Grouped Greedy Equivalence Search (GGES). The impact of each variable on
workday loss was assessed through intervention do-calculus adjustment (IDA)
scores. Model training and validation were performed using the 10-fold
cross-validation technique. Performance metrics, including adjacency precision
(AP), adjacency recall (AR), arrowhead precision (AHP), and arrowhead recall
(AHR), were utilized to evaluate the models. Findings revealed that after 2006,
key direct causes of workday loss among mining employees included total mining
experience, mean office employees, mean underground employees, county, and
total mining experience (years). Total mining experience emerged as the most
influential factor, whereas mean employees per mine exhibited the least
influence. The analyses emphasized the significant role of total mining
experience in determining workday loss. The models achieved optimal
performance, with AP, AR, AHP, and AHR values measuring 0.694, 0.653, 0.386,
and 0.345, respectively. This study demonstrates the feasibility of utilizing
the new GGES method to clarify the causal factors behind the workday loss by
analyzing employment demographics and injury records and establish their causal
relationship network.