Given a dataset of individuals each described by a covariate vector, a
treatment, and an observed outcome on the treatment, the goal of the individual
treatment effect (ITE) estimation task is to predict outcome changes resulting
from a change in treatment. A fundamental challenge is that in the
observational data, a covariate's outcome is observed only under one treatment,
whereas we need to infer the difference in outcomes under two different
treatments. Several existing approaches address this issue through training
with inferred pseudo-outcomes, but their success relies on the quality of these
pseudo-outcomes. We propose PairNet, a novel ITE estimation training strategy
that minimizes losses over pairs of examples based on their factual observed
outcomes. Theoretical analysis for binary treatments reveals that PairNet is a
consistent estimator of ITE risk, and achieves smaller generalization error
than baseline models. Empirical comparison with thirteen existing methods
across eight benchmarks, covering both discrete and continuous treatments,
shows that PairNet achieves significantly lower ITE error compared to the
baselines. Also, it is model-agnostic and easy to implement.

通过对成对示例基于其已观测到的实际结果最小化损失的训练策略，PairNet 能够准确地预测处理变化导致的结果变化，并在多个基准测试中表现出比基准模型更小的 ITE 误差，同时具有模型不可知性和易于实现的优点。

PairNet: 使用观察到的对作为训练样本以估计个体处理效果

PairNet: Training with Observed Pairs to Estimate Individual Treatment  Effect

Longitudinal studies, where a series of images from the same set of
individuals are acquired at different time-points, represent a popular
technique for studying and characterizing temporal dynamics in biomedical
applications. The classical approach for longitudinal comparison involves
normalizing for nuisance variations, such as image orientation or contrast
differences, via pre-processing. Statistical analysis is, in turn, conducted to
detect changes of interest, either at the individual or population level. This
classical approach can suffer from pre-processing issues and limitations of the
statistical modeling. For example, normalizing for nuisance variation might be
hard in settings where there are a lot of idiosyncratic changes. In this paper,
we present a simple machine learning-based approach that can alleviate these
issues. In our approach, we train a deep learning model (called PaIRNet, for
Pairwise Image Ranking Network) to compare pairs of longitudinal images, with
or without supervision. In the self-supervised setup, for instance, the model
is trained to temporally order the images, which requires learning to recognize
time-irreversible changes. Our results from four datasets demonstrate that
PaIRNet can be very effective in localizing and quantifying meaningful
longitudinal changes while discounting nuisance variation. Our code is
available at
https://github.com/heejong-kim/learning-to-compare-longitudinal-images.git

本文提出了一种使用深度学习模型的机器学习方法 (PaIRNet)，用于比较序列图像，从而定位和量化有意义的纵向变化并降低噪声干扰。