The unit selection problem defined by Li and Pearl identifies individuals who
have desired counterfactual behavior patterns, for example, individuals who
would respond positively if encouraged and would not otherwise. Li and Pearl
showed by example that their unit selection model is beyond the A/B test
heuristics. In this paper, we reveal the essence of the A/B test heuristics,
which are exceptional cases of the benefit function defined by Li and Pearl.
Furthermore, We provided more simulated use cases of Li-Pearl's unit selection
model to help decision-makers apply their model correctly, explaining that A/B
test heuristics are generally problematic.

该研究介绍了 Li 和 Pearl 所定义的单元选择问题，针对有所需反事实行为模式的人进行筛选，并且比较了该模型与 A/B 测试的区别和优越性，同时提供了更多模拟用例，并指出 A/B 测试普遍存在问题。

单元选择：案例研究及与 A/B 测试启发式算法的比较

Unit Selection: Case Study and Comparison with A/B Test Heuristic

In many areas, we have well-founded insights about causal structure that
would be useful to bring into our trained models while still allowing them to
learn in a data-driven fashion. To achieve this, we present the new method of
interchange intervention training (IIT). In IIT, we (1) align variables in a
causal model (e.g., a deterministic program or Bayesian network) with
representations in a neural model and (2) train the neural model to match the
counterfactual behavior of the causal model on a base input when aligned
representations in both models are set to be the value they would be for a
source input. IIT is fully differentiable, flexibly combines with other
objectives, and guarantees that the target causal model is a causal abstraction
of the neural model when its loss is zero. We evaluate IIT on a structural
vision task (MNIST-PVR), a navigational language task (ReaSCAN), and a natural
language inference task (MQNLI). We compare IIT against multi-task training
objectives and data augmentation. In all our experiments, IIT achieves the best
results and produces neural models that are more interpretable in the sense
that they more successfully realize the target causal model.

通过交替干预训练的方法，将神经模型与因果模型进行对齐并将其纳入训练，实现在保留数据驱动学习能力的同时较好地表达因果结构，并在结构视觉、语言导航和自然语言推理任务上通过与多任务训练目标和数据增强的对比达到较好的效果，制备了较好的解释性神经模型。