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

Designing faithful yet accurate AI models is challenging, particularly in the
field of individual treatment effect estimation (ITE). ITE prediction models
deployed in critical settings such as healthcare should ideally be (i)
accurate, and (ii) provide faithful explanations. However, current solutions
are inadequate: state-of-the-art black-box models do not supply explanations,
post-hoc explainers for black-box models lack faithfulness guarantees, and
self-interpretable models greatly compromise accuracy. To address these issues,
we propose DISCRET, a self-interpretable ITE framework that synthesizes
faithful, rule-based explanations for each sample. A key insight behind DISCRET
is that explanations can serve dually as database queries to identify similar
subgroups of samples. We provide a novel RL algorithm to efficiently synthesize
these explanations from a large search space. We evaluate DISCRET on diverse
tasks involving tabular, image, and text data. DISCRET outperforms the best
self-interpretable models and has accuracy comparable to the best black-box
models while providing faithful explanations. DISCRET is available at
this https URL

为解决诸如黑箱模型无法提供解释、后期解释器缺乏忠实性保证以及自解释模型极大损害准确性等问题，我们提出了 DISCRET，一个自解释的个体治疗效应估计框架，通过从大搜索空间中高效地合成忠实的基于规则的解释来有效地解决该问题，并在涉及表格、图像和文本数据的多样任务中对其进行了评估，证明其优于最佳自解释模型，并且提供了与最佳黑箱模型相媲美的准确性和忠实的解释。DISCRET 可在此链接处获得。