Applying Inverse Propensity Score Weighting and Doubly Robust for Treatment Effect Estimation in Sepsis

Abstract

Sepsis is a severe disease with a relatively high fatality rate. The treatment assignment of antibiotics is one of the most crucial factors in reducing the mortality rate. Existing machine learning models are designed to estimate the individual time-to-treatment effect of the antibiotic. However, most models need more efficiency and stability in overcoming the selection bias problem. This paper applies two methods of debiasing samples and estimating individual treatment effects. By re-weighting, we create pseudo mini-batches that mimic the corresponding randomized controlled trial (RCT) process. We also adopt Inverse Propensity Score Weighting (IPW) method to assign appropriate weight to each sample in the observation dataset by estimating the propensity scores. We assume Doubly Robust (DR) to combine IPW and outcome regression model. On a real-world dataset, the experiments exhibit the ability of the model to successfully identify effective timing of treatment.