研究贝叶斯因果学习中先验的作用，发现无标签数据无助于参数估计。
On the Role of Priors in Bayesian Causal Learning
- 从贝叶斯视角分析独立因果机制的因果学习方法
- 无标签数据无法提升机制参数估计效果
- 合适的先验可保证后验因子分解，契合因果独立性定义
本文从贝叶斯角度研究独立因果机制的因果学习。确认了文献中的已有结论：无标签数据（即原因实现）无法改善定义机制的参数估计。此外，我们观察到对原因和机制参数选择合适先验的重要性。具体而言，因子分解先验会导致因子分解后验，这与Janzing和Schölkopf通过分布的Kolmogorov复杂度定义的独立因果机制一致，并符合Heckerman等人提出的参数独立性概念。
原文摘要 · Abstract (English)
In this work, we investigate causal learning of independent causal mechanisms from a Bayesian perspective. Confirming previous claims from the literature, we show in a didactically accessible manner that unlabeled data (i.e., cause realizations) do not improve the estimation of the parameters defining the mechanism. Furthermore, we observe the importance of choosing an appropriate prior for the cause and mechanism parameters, respectively. Specifically, we show that a factorized prior results in a factorized posterior, which resonates with Janzing and Schölkopf's definition of independent causal mechanisms via the Kolmogorov complexity of the involved distributions and with the concept of parameter independence of Heckerman et al.
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