Multi-treatment Effect Estimation from Biomedical Data

Several biomedical applications contain multiple treatments from which we want to estimate the causal effect on a given outcome. Most existing Causal Inference methods, however, focus on single treatments. In this work, we propose a neural network that adopts a multi-task learning approach to estimate the effect of multiple treatments. We validated M3E2 in three synthetic benchmark datasets that mimic biomedical datasets. Our analysis showed that our method makes more accurate estimations than existing baselines.