DISCRET：为处理效应估计合成忠实解释
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
AI总结：
针对个体处理效应估计中黑盒模型无解释、自解释模型精度低的问题，提出自解释框架DISCRET，利用RL算法合成基于规则的忠实解释，准确率媲美黑盒模型。
摘要：
设计既忠实又准确的AI模型极具挑战，尤其是在个体处理效应估计（ITE）领域。部署在医疗等关键场景中的ITE预测模型理想情况下应具备（i）准确性，以及（ii）提供忠实解释。然而，现有方案均显不足：最先进的黑盒模型不提供解释，黑盒模型的事后解释器缺乏忠实性保证，而自解释模型则极大牺牲了准确性。为解决这些问题，我们提出了DISCRET，这是一个自解释的ITE框架，能为每个样本合成忠实的基于规则的解释。DISCRET背后的一个关键洞见是，解释可以双重作为数据库查询，以识别相似的样本子群。我们提供了一种新颖的RL算法，以从庞大的搜索空间中高效合成这些解释。我们在涉及表格、图像和文本数据的多样化任务上评估了DISCRET。DISCRET超越了最优的自解释模型，且准确率可媲美最优的黑盒模型，同时提供忠实解释。DISCRET已在https://github.com/wuyinjun-1993/DISCRET-ICML2024开源。
英文摘要：
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 https://github.com/wuyinjun-1993/DISCRET-ICML2024.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
