Causal effect estimation from data typically requires assumptions about the cause-effect relations either explicitly in the form of a causal graph structure within the Pearlian framework, or implicitly in terms of (conditional) independence statements between counterfactual variables within the potential outcomes framework. When the treatment variable and the outcome variable are confounded, front-door adjustment is an important special case where, given the graph, causal effect of the treatment on the target can be estimated using post-treatment variables. However, the exact formula for front-door adjustment depends on the structure of the graph, which is difficult to learn in practice. In this work, we provide testable conditional independence statements to compute the causal effect using front-door-like adjustment without knowing the graph under limited structural side information. We show that our method is applicable in scenarios where knowing the Markov equivalence class is not sufficient for causal effect estimation. We demonstrate the effectiveness of our method on a class of random graphs as well as real causal fairness benchmarks.

翻译：从数据中估计因果效应通常需要对因果关系做出假设，这些假设要么以皮尔逊框架中的因果图结构形式明确给出，要么以潜在结果框架中反事实变量之间的（条件）独立性陈述隐含表达。当处理变量和结果变量存在混杂时，前门调整是一种重要特例：在已知图结构的情况下，可以利用处理后变量估计处理对目标的因果效应。然而，前门调整的精确公式依赖于图结构，而实践中学习图结构十分困难。本研究提出了可检验的条件独立性陈述，在仅具备有限结构性辅助信息而无需知道完整图结构的情况下，通过类似前门调整的方式计算因果效应。我们证明，当仅知马尔可夫等价类不足以进行因果效应估计时，该方法依然适用。我们在一类随机图以及真实因果公平性基准测试中验证了该方法的有效性。