论文标题
未知因果干预措施的混合物
Disentangling Mixtures of Unknown Causal Interventions
论文作者
论文摘要
在许多实际情况下，例如基因敲除实验，靶向干预措施通常伴随着脱离靶向地点的未知干预措施。此外，不同单元可以随机接触到不同的未知干预措施，从而产生干预措施的混合。在某些应用中，识别这种混合物的不同组件可能非常有价值。在这种情况下，在这项工作中，我们研究了确定给定因果贝叶斯网络中干预措施中所有组件的问题。我们构建了一个示例，以表明总体而言，这些组件是从混合分布中识别的。接下来，假设给定的网络满足阳性条件，我们表明，如果一组混合组件满足温和的排除假设，则可以唯一地识别它们。我们的证明提供了有效的算法，可以从可能目标的指数较大的搜索空间中恢复这些目标。在更现实的情况下，通过有限的许多样本给出了分布，我们进行了一项仿真研究，以分析从我们的可识别性证明中得出的算法的性能。
In many real-world scenarios, such as gene knockout experiments, targeted interventions are often accompanied by unknown interventions at off-target sites. Moreover, different units can get randomly exposed to different unknown interventions, thereby creating a mixture of interventions. Identifying different components of this mixture can be very valuable in some applications. Motivated by such situations, in this work, we study the problem of identifying all components present in a mixture of interventions on a given causal Bayesian Network. We construct an example to show that, in general, the components are not identifiable from the mixture distribution. Next, assuming that the given network satisfies a positivity condition, we show that, if the set of mixture components satisfy a mild exclusion assumption, then they can be uniquely identified. Our proof gives an efficient algorithm to recover these targets from the exponentially large search space of possible targets. In the more realistic scenario, where distributions are given via finitely many samples, we conduct a simulation study to analyze the performance of an algorithm derived from our identifiability proof.
