Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example. In interventional settings, such as perturbation experiments in genomics, exchangeability often holds only within subsets of interventions that leave a target variable "unaffected" (e.g., non-descendants of an intervened node in a causal graph). We study the practical regime where this invariance structure is unknown and must be learned from data. Our contributions are: (i) a contamination-robust conformal coverage theorem that quantifies how misclassification of "unaffected" calibration examples degrades coverage via an explicit function $g(δ,n)$ of the contamination fraction and calibration set size, providing a finite-sample lower bound that holds for arbitrary contaminating distributions; (ii) a task-driven partial causal learning formulation that estimates only the binary descendant indicators $Z_{a,i}=\mathbf{1}\{i\in\mathrm{desc}(a)\}$ needed for selective calibration, rather than the full causal graph; and (iii) algorithms for descendant discovery via perturbation intersection patterns (differentially affected variable set intersections across interventions), and for approximate distance-to-intervention estimation via local invariant causal prediction. We provide recovery conditions under which contamination is controlled. Experiments on synthetic linear structural equation models (SEMs) validate the bound: under controlled contamination up to $δ=0.30$, the corrected procedure maintains $\ge 0.95$ coverage while uncorrected selective CP degrades to $0.867$. A proof-of-concept on Replogle K562 CRISPR interference (CRISPRi) perturbation data demonstrates applicability to real genomic screens.

翻译：选择性共形预测能够显著缩小不确定性集合，前提是我们能够识别与测试样本可交换的校准样本。在干预性场景中（例如基因组学中的扰动实验），可交换性通常仅存在于那些使目标变量“不受影响”的干预子集内（例如，因果图中被干预节点的非后代节点）。我们研究该不变性结构未知且必须从数据中学习的实际情形。我们的贡献包括：（i）一个污染鲁棒性共形覆盖定理，该定理通过污染比例与校准集大小的显式函数 $g(δ,n)$ 量化了“不受影响”校准样本的错误分类如何降低覆盖概率，提供了一个对任意污染分布均成立的有限样本下界；（ii）一种任务驱动的部分因果学习框架，该框架仅估计选择性校准所需的二元后代指示变量 $Z_{a,i}=\mathbf{1}\{i\in\mathrm{desc}(a)\}$，而非完整的因果图；以及（iii）通过扰动交集模式（不同干预下差异受影响变量集的交集）进行后代发现的算法，以及通过局部不变因果预测进行近似干预距离估计的算法。我们提供了控制污染所需的恢复条件。在线性结构方程模型（SEMs）上的合成实验验证了该界：在污染比例高达 $δ=0.30$ 的受控条件下，校正后的方法保持了 $\ge 0.95$ 的覆盖概率，而未校正的选择性共形预测则下降至 $0.867$。在Replogle K562 CRISPR干扰（CRISPRi）扰动数据上的概念验证证明了该方法在真实基因组筛选中的适用性。