这篇论文换了个思路：别死磕条件独立性检验，用干预信息直接做因果发现，还放宽了忠实性假设，挺颠覆的。
该论文针对因果发现中传统忠实性假设（faithfulness）导致错误移除真实因果依赖的问题，提出干预-即时忠实性（intervention-immediacy faithfulness）这一更温和的假设。该假设允许因果路径相互抵消，仅利用硬干预（hard interventions）即可非参数地识别因果结构。实验表明，相比依赖条件独立性检验的两阶段方法，干预信息能更可靠地判断因果链接的存在与否。论文还给出了当干预范围不足时识别条件的等价类。
Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed causal relationships, then applying interventions to orient the unknown causal directions. A critical assumption for the first step is faithfulness: a requirement that causally linked variables exhibit statistical dependence. Many natural systems include buffering and stabilizing pathways that cancel out to achieve systemic robustness. This cancellation of pathways violates faithfulness, leading causal discovery algorithms to incorrectly remove causal dependencies. In this paper, we argue that hard interventions contain information about the presence/absence of causal linkage that is overlooked in the first stage of structure discovery. We show that a mild assumption -- called intervention-immediacy faithfulness -- that allows cancellations, is sufficient to nonparametrically identify causal structures with hard interventions. These results position interventions as the primary carriers of information about causal structure, which should take precedence over conditional independence testing. To flip the paradigm, we also specify equivalence classes when the identification criteria are not met due to limitations in the scope of interventions.