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# Estimating individual-level optimal causal interventions combining causal models and machine learning models

*Proceedings of The KDD'21 Workshop on Causal Discovery*, PMLR 150:55-77, 2021.

#### Abstract

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.