<![CDATA[Child stunting remains a major public health challenge, reflecting the long-term effects of inadequate nutrition, limited maternal education, and restricted access to health services. However, most existing studies rely on correlational analysis, leaving the underlying causal mechanisms insufficiently understood. This gap limits the development of effective interventions, as policymakers require evidence on how determinants interact causally. To address this issue, this study applies two causal discovery algorithms Greedy Equivalence Search (GES) and Peter Clark (PC) to identify causal relationships among eight key determinants of stunting using secondary data from the West Bangka District Health Office (2024). The variables include anthropometric indicators, maternal characteristics, and environmental conditions. Causal assumptions such as causal sufficiency, acyclicity, and faithfulness were imposed to ensure identifiability of the resulting graphs. Model performance was evaluated using Directed Density (DD) and Causal Density (CD) metrics. GES generated a parsimonious causal structure highlighting maternal education, posyandu visits, and exclusive breastfeeding as dominant causal candidates affecting height-for-age (TB/U) and weight-for-age (BB/U). In contrast, the PC algorithm produced a more complete and dense structure, achieving DD = 1.0 and CD = 0.12, compared with GES (DD = 0.80; CD = 0.10). These results indicate that PC is more exploratory in mapping complex causal interactions, while GES offers a simpler and more conservative model. Collectively, the findings demonstrate that combining score-based and constraint-based discovery approaches yields complementary insights into the mechanisms driving stunting.]]>
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