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

Abstract

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: = 0.002), but this does not mean MAS directly causes LBW. MAS likely results from fetal distress or other pregnancy complications that may independently affect LBW. While statistical associations exist, clinical causation remains unproven; therefore, the counterfactual analysis showed MLCC could help reduce LBW risk. CFR-WASS achieved high accuracy (84%) while the precision in heterogeneous treatment effect (PEHE = 1.006) and the average treatment effect (ATE = 0.24), and CFR-MMD PEHE of 1.02, ATE of 0.45, demonstrating potential for tailored treatment strategies. DNN and multilayer perceptrons uniquely identified key neural weights and biases favoring normal birth weight while suppressing LBW predictions, offering interpretable insights for clinical risk assessment. 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.