Causal effects of Antenatal Care (ANC) on child malnutrition: a machine learning approach in Ethiopia and Rwanda

Malnutrition among children under five remains a critical public health challenge in Ethiopia and Rwanda, with nearly half of the children in this study population affected by at least one form of undernutrition. This study provides a robust causal analysis of the heterogeneous effects of Antenatal Care (ANC) on child malnutrition, as measured by the Composite Index of Anthropometric Failure (CIAF). Leveraging data from 33,737 mother-child pairs from the Demographic and Health Surveys (DHS) in Ethiopia (N = 22,668) and Rwanda (N = 11,069) across three waves (2005-2015), we employ a machine learning-based Causal Forest model. This approach was chosen specifically to overcome the limitations of traditional regression methods, allowing for the estimation of how the impact of different levels of ANC attendance varies across diverse sociodemographic and health-related subgroups. The results reveal a clear and powerful dose-response relationship. While a single ANC visit has a negligible effect, attending 2-3 visits is associated with a modest 3.5% point reduction in the risk of malnutrition. The strongest impact is seen with the completion of 4 or more visits, which is associated with an average reduction of 5.7% points (ATE: -0.057). Crucially, this average effect masks profound and policy-relevant heterogeneity. The benefits of ANC are massively amplified for the most vulnerable populations; for children in the poorer wealth quintile, 4 + ANC visits are associated with a massive 17.1% point reduction in malnutrition risk-an effect nearly three times the population average. The intervention is also particularly impactful for older mothers (aged 35-49), where it is associated with an 11.9% point risk reduction. Furthermore, the benefits of ANC are amplified by a healthier environment; for children in households with an improved water source, the associated risk reduction is a substantial 7.8% points, demonstrating a powerful synergistic effect between clinical care and public health infrastructure. The application of the Causal Forest model represents a significant advancement, moving beyond a single average effect to uncover this critical heterogeneity and identify for whom, and under what conditions, ANC is most effective. These findings provide robust causal evidence for a necessary shift away from a one-size-fits-all public health strategy. The results strongly advocate for stratified and multi-sectoral interventions. Policy and resources should be intensely focused on ensuring the most vulnerable populations-particularly the poorest households and older mothers-complete the full ANC schedule, as this is where the public health return on investment is highest. Additionally, the synergistic effect with WASH highlights the need to integrate clinical maternal health programs with investments in community-level water and sanitation infrastructure. By tailoring ANC programs and combining them with broader public health improvements, governments can develop more holistic and effective strategies to accelerate progress against child malnutrition in Ethiopia, Rwanda, and comparable high-burden contexts.