Estimation of treatment efficacy of real-world clinical interventions
involves working with continuous outcomes such as time-to-death,
re-hospitalization, or a composite event that may be subject to censoring.
Counterfactual reasoning in such scenarios requires decoupling the effects of
confounding physiological characteristics that affect baseline survival rates
from the effects of the interventions being assessed. In this paper, we present
a latent variable approach to model heterogeneous treatment effects by
proposing that an individual can belong to one of latent clusters with distinct
response characteristics. We show that this latent structure can mediate the
base survival rates and helps determine the effects of an intervention. We
demonstrate the ability of our approach to discover actionable phenotypes of
individuals based on their treatment response on multiple large randomized
clinical trials originally conducted to assess appropriate treatments to reduce
cardiovascular risk.

本文提出了一种潜变量方法，用于模拟异质性治疗效果，并演示其在多个大型随机对照试验中发现个体可操作表型的能力。

带有截尾时间事件的反事实表型学

Counterfactual Phenotyping with Censored Time-to-Events

Many real-world tasks require models to compare images along multiple
similarity conditions (e.g. similarity in color, category or shape). Existing
methods often reason about these complex similarity relationships by learning
condition-aware embeddings. While such embeddings aid models in learning
different notions of similarity, they also limit their capability to generalize
to unseen categories since they require explicit labels at test time. To
address this deficiency, we propose an approach that jointly learns
representations for the different similarity conditions and their contributions
as a latent variable without explicit supervision. Comprehensive experiments
across three datasets, Polyvore-Outfits, Maryland-Polyvore and UT-Zappos50k,
demonstrate the effectiveness of our approach: our model outperforms the
state-of-the-art methods, even those that are strongly supervised with
pre-defined similarity conditions, on fill-in-the-blank, outfit compatibility
prediction and triplet prediction tasks. Finally, we show that our model learns
different visually-relevant semantic sub-spaces that allow it to generalize
well to unseen categories.

提出一种联合学习多个相似性条件的表示及其贡献的潜变量的方法，能够有效地处理图片比较问题并兼顾泛化能力。