Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer

arXiv:2404.04399v1 Announce Type: cross
Abstract: We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings. Our approach utilizes a transformer architecture with heterogeneous type embedding trained using temporal-difference learning. After obtaining an initial estimate using the transformer, following the targeted minimum loss-based likelihood estimation (TMLE) framework, we statistically corrected for the bias commonly associated with machine learning algorithms. Furthermore, our method also …

abstract architecture arxiv counterfactual cs.ai cs.lg difference dynamic embedding loss mean novel policies stat.ap stat.me stat.ml temporal temporal-difference transformer transformer architecture treatment type