Causal models are crucial for understanding complex systems and identifying
causal relationships among variables. Even though causal models are extremely
popular, conditional probability calculation of formulas involving
interventions pose significant challenges. In case of Causal Bayesian Networks
(CBNs), Pearl assumes autonomy of mechanisms that determine interventions to
calculate a range of probabilities. We show that by making simple yet often
realistic independence assumptions, it is possible to uniquely estimate the
probability of an interventional formula (including the well-studied notions of
probability of sufficiency and necessity). We discuss when these assumptions
are appropriate. Importantly, in many cases of interest, when the assumptions
are appropriate, these probability estimates can be evaluated using
observational data, which carries immense significance in scenarios where
conducting experiments is impractical or unfeasible.

通过简单但常见的独立性假设，可以独特地估计干预公式的概率，包括充分性和必要性的概率。在许多感兴趣的情况下，当这些假设适用时，可以使用观测数据评估这些概率估计，这在实验不可行的情况下具有巨大的重要性。