Upcoming seminars

Monday, July 29, 15:15

Toru Shirakawa
Osaka University Graduate School of Medicine, Japan, and The Center for Targeted Machine Learning and Causal Inference at UC Berkeley, USA
Deep LTMLE: Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer

We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the mean of counterfactual 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 facilitates statistical inference by enabling the provision of 95% confidence intervals grounded in asymptotic statistical theory. Simulation results demonstrate our method’s competitive performance with existing approaches for simple settings and superior computational performance, particularly in complex, long time-horizon scenarios. It remains effective in small-sample, short-duration contexts, matching the performance of asymptotically efficient estimators. To demonstrate our method in practice, we applied our method to estimate counterfactual mean outcomes for standard versus intensive blood pressure management strategies in a real-world cardiovascular epidemiology cohort study. I will also present the continuous-time extension of the Deep LTMLE.

Room: 5.2.46 (Biostats library)

Map of CSS

You can find CSS next to the Botanical Garden, 5 minutes from Nørreport station.


Meeting room 5.2.46 is the library of the Biostatistics section, located in building 5, 2nd floor, room 46. See the map below for directions inside CSS.