PROJECT SUMMARY Personalized intervention in a heterogeneous population requires refined methods and analytics to estimate the treatment effects of dynamic interventions from longitudinal medical history, in particular, to choose the optimal individual intervention that maximizes the expectation of long-term future outcomes. The g-formula, in theory, solves this longitudinal causal inference problem with multiple time point interventions and time- dependent confounding.1 The causal target parameter under dynamic intervention is identified through the g- functional of distributions of observables.2 We have developed the longitudinal target minimum loss-based estimator (LTMLE)3, a semiparametric doubly- robust efficient plug-in estimator for g-functionals. We have implemented it as an R package ltmle4. LTMLE requires nuisance parameter estimators with fast convergence rates. We have developed a highly adaptive lasso (HAL) theory with sufficient convergence rates.5–7 We have implemented R packages hal90018 for general HAL estimators and haldensify9 for conditional density estimations with HAL. LTMLE with g- computation estimating the full likelihood of observables using HAL provides a more generative approach that is compatible with modern generative artificial intelligence (AI) yet has a statistical guarantee. However, these packages suffer from computational inefficiency when the medical data becomes very large. This is because our current software is built upon the standard central processing unit (CPU) that has limited computational capacity for calculus and linear algebra. Recent programming libraries in Python for efficient vectorized computation with graphic processing units (GPUs) or tensor processing units (TPUs) can solve this scalability issue. Therefore, we will implement LTMLE and HAL in Python with Google JAX11, which optimizes auto differential (autograd) and array computations for CPUs, GPUs, and TPUs. Designing software friendly to data scientists is essential for the sustainability of the software. Causal inference from longitudinal observational data is also the central problem in the field of reinforcement learning (RL)12. Estimation of the mean counterfactual outcome under dynamic interventions with time-dependent confounding is called off-policy evaluation (OPE) in the offline RL literature.13 We will design the software friendly to researchers from both fields and guide users with vignettes with their familiar scenarios for smooth adoption. We set the following three aims. Aim 1: Develop efficient implementation of g-computation of LTMLE with HAL for survival outcome using Google JAX. Aim 2: Design user-friendly LTMLE software for biomedical researchers and data scientists. Aim 3: Present the software and its performance and application at academic conferences in epidemiology and computer science.