Deep learning for causal inference using low birth weight in midwife-led continuity care intervention in north Shoa zone, Ethiopia.

Introduction: Low birth weight (LBW), under 2,500 g, poses health risks, though not always requiring treatment. Early detection of high-risk pregnancies enables preventive care, improving outcomes for mother and baby. This study aimed to establish cause-and-effect relationships using Causal Deep Learning (CDL) models that reduce bias and estimate heterogeneous treatment effects on LBW in the Midwife-Led Continuity Care (MLCC) intervention. Methods: This study used a quasi-experimental study design (August 2019-September 2020) in North Shoa, Ethiopia, and enrolled 1,166 women divided into two groups: one receiving MLCC and the other receiving other professional groups for comprehensive antenatal/postnatal care. The dataset and code are provided in data availability section. Our model combines counterfactual convolutional neural networks to analyze time-based patterns and Bayesian Ridge regression to reduce bias in propensity scores. We use Counterfactual Regression with Wasserstein Distance (CFR-WASS) and Counterfactual Regression with Maximum Mean Discrepancy (CFR-MMD) to balance patient characteristics and improve counterfactual estimates of treatment effects. This approach strengthens causal insights into how MLCC interventions affect LBW outcomes. Result: The Deep neural networks (DNN) model showed strong predictive accuracy for LBW, with 81.3% training and 81.4% testing performance, an area under the curve (AUC) of 0.88, enabling the reliable early identification of high-risk pregnancies. The study found a strong link between meconium aspiration syndrome (MAS) and LBW ( Conclusion: The CFR-WASS/CFR-MMD model strengthens LBW prediction by identifying crucial factors like MAS and healthcare access, while accurate PEHE and ATE estimates support data-driven prenatal care and targeted interventions for healthier outcomes.