Cyclic Causal Discovery from Continuous Equilibrium Data
Cyclic Causal Discovery from Continuous Equilibrium Data
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发表时间：
2013-08
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通讯作者：
J. Mooij;T. Heskes
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作者：
J. Mooij;T. Heskes
我们提出一种从观测和干预平衡数据的组合中学习循环因果模型的方法。该方法的新颖之处在于它能够处理连续数据（不假设线性）以及处理反馈回路。在生化反应的背景下，我们还提出一种对干预进行建模的新方法，这种方法改变化合物的活性而非其丰度。出于计算原因，我们通过（耦合的）局部线性化来近似非线性因果机制，每个实验条件对应一个局部线性化。我们将该方法应用于从萨克斯等人（2005年）测量的流式细胞术数据中重建细胞信号网络。我们表明，我们的方法在数据中找到了反馈回路的证据，并且在可比的模型复杂度下对数据给出了更准确的定量描述。
We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data. Novel aspects of the proposed method are its ability to work with continuous data (without assuming linearity) and to deal with feedback loops. Within the context of biochemical reactions, we also propose a novel way of modeling interventions that modify the activity of compounds instead of their abundance. For computational reasons, we approximate the nonlinear causal mechanisms by (coupled) local linearizations, one for each experimental condition. We apply the method to reconstruct a cellular signaling network from the flow cytometry data measured by Sachs et al. (2005). We show that our method finds evidence in the data for feedback loops and that it gives a more accurate quantitative description of the data at comparable model complexity.