In the context of medical decision making, counterfactual prediction enables
clinicians to predict treatment outcomes of interest under alternative courses
of therapeutic actions given observed patient history. Prior machine learning
approaches for counterfactual predictions under time-varying treatments focus
on static time-varying treatment regimes where treatments do not depend on
previous covariate history. In this work, we present G-Transformer, a
Transformer-based framework supporting g-computation for counterfactual
prediction under dynamic and time-varying treatment strategies. G-Transfomer
captures complex, long-range dependencies in time-varying covariates using a
Transformer architecture. G-Transformer estimates the conditional distribution
of relevant covariates given covariate and treatment history at each time point
using an encoder architecture, then produces Monte Carlo estimates of
counterfactual outcomes by simulating forward patient trajectories under
treatment strategies of interest. We evaluate G-Transformer extensively using
two simulated longitudinal datasets from mechanistic models, and a real-world
sepsis ICU dataset from MIMIC-IV. G-Transformer outperforms both classical and
state-of-the-art counterfactual prediction models in these settings. To the
best of our knowledge, this is the first Transformer-based architecture for
counterfactual outcome prediction under dynamic and time-varying treatment
strategies. Code will be released upon publication of the paper.

基于 G-Transformer 框架的对动态和时间变化的治疗策略进行反事实预测的研究中，通过模拟患者在感兴趣的治疗策略下的前进轨迹，使用 Transformer 架构捕获时间变化的协变量的复杂长程依赖并评估该框架的性能。

G-Transformer：动态和时变治疗制度下的反事实结果预测

G-Transformer: Counterfactual Outcome Prediction under Dynamic and  Time-varying Treatment Regimes

We explore Reconstruction Robustness (ReRo), which was recently proposed as
an upper bound on the success of data reconstruction attacks against machine
learning models. Previous research has demonstrated that differential privacy
(DP) mechanisms also provide ReRo, but so far, only asymptotic Monte Carlo
estimates of a tight ReRo bound have been shown. Directly computable ReRo
bounds for general DP mechanisms are thus desirable. In this work, we establish
a connection between hypothesis testing DP and ReRo and derive closed-form,
analytic or numerical ReRo bounds for the Laplace and Gaussian mechanisms and
their subsampled variants.

我们建立了假设检验差分隐私与重建鲁棒性的联系，并推导出来自拉普拉斯和高斯机制以及其子采样变体的闭式、解析或数值重建鲁棒性上界。