Causal Relationship Network of Risk Factors Impacting Workday Loss in Underground Coal Mines

Abstract

Purpose: To explore relationships with post-event workday loss in U.S. underground coal mines using Grouped Greedy Equivalence Search (GGES), a prior-knowledge-constrained extension of GES. Methods: The analysis utilizes data obtained from the National Institute for Occupational Safety and Health (NIOSH). We analyzed 101,010 reported injury/illness events linked to annual employment records from 3982 unique underground coal mines spanning the years from 1990 to 2020. Experts selected the variables and consolidated nominal categories. GGES was applied to the full dataset and two descriptive periods, 1990–2005 and 2006–2020. Intervention calculus when the DAG is absent (IDA) derived total causal effect (TCE) values, which were retained as exploratory model summaries, without cross-variable ranking. Record-level 10-fold splits assessed structural agreement using adjacency precision (AP), adjacency recall (AR), arrowhead precision (AHP), and arrowhead recall (AHR). Results: In the 2006–2020 network, variables adjacent to workday loss included total mining experience in its raw years-weeks and decimal-year representations, mean office employees, mean underground employees, and the MSHA district/inspection-office code. The two experience representations describe one construct and are not independent confirmations. The full-period marginal correlations with experience were small and positive (r = 0.032 and r = 0.069). For 2006–2020, AP and AR were 0.694 and 0.653, while AHP and AHR were 0.386 and 0.345. These values describe agreement across record partitions that may share mines, not validation in unseen mines or against causal ground truth. Conclusions: The analysis organizes candidate associations with the number of workdays lost after a reported injury or illness event; it does not estimate accident incidence. Under the stated assumptions, the network provides exploratory causal hypotheses for further study. Mine-grouped validation, analyses accounting for within-mine dependence, and single-encoding sensitivity analyses are needed before stronger causal or operational conclusions are drawn.