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。