Estimating individual-level optimal causal interventions combining causal models and machine learning models
Estimating individual-level optimal causal interventions combining causal models and machine learning models
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发表时间：
2021
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通讯作者：
Keisuke Kiritoshi;Tomonori Izumitani;Kazuki Koyama;Tomomi Okawachi;Keisuke Asahara;Shohei Shimizu
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作者：
Keisuke Kiritoshi;Tomonori Izumitani;Kazuki Koyama;Tomomi Okawachi;Keisuke Asahara;Shohei Shimizu
我们引入了一种新的统计因果推理方法来估计个体层面的最优因果干预，即我们应该将个体的某个变量的值设置到哪个值，以获得另一个变量的期望值。这被定义为一个优化问题，以最小化期望值与在个人设置下可能达到的值之间的误差。为了解决优化问题，我们首先训练一个机器学习模型来预测一个目标变量的值，然后估计变量的因果结构。然后，我们将机器学习模型和因果结构结合到一个单一的因果模型中，以估计预测的目标变量的反事实值。这对于更准确地估计个人层面的最佳因果干预是有效的。我们进一步提出了一种梯度下降算法来计算最优因果干预。我们的方法一般适用于线性和非线性相关的连续变量。在实验中，我们使用非线性因果结构生成的人工数据和真实数据来评估我们方法的有效性。
We introduce a new statistical causal inference method to estimate individual-level optimal causal intervention , that is, to which value we should set the value of a certain variable of an individual to obtain a desired value of another variable. This is defined as an optimization problem to minimize the error between a desired value and the value that would have been attained under the setting for the individual. To solve the optimization problem, we first train a machine learning model to predict the value of an objective variable and then estimate the causal structure of variables. We then combine the machine learning model and causal structure into a single causal model to estimate counterfactual value of the predicted objective variable. This is effective in achieving a more accurate estimation of individual-level optimal causal intervention. We further propose a gradient descent algorithm to compute the optimal causal intervention. Our method is generally applicable to continuous variables that are linearly and non-linearly related. In experiments, we evaluate the effectiveness of our method using artificial data generated by non-linear causal structures and real data.