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The most widely discussed methods for estimating the Average CausalEffect/Average Treatment Effect are those for intervention in discrete binaryvariables whose value represents intervention/non-intervention groups. On theother hand, methods for intervening in continuous variables independent of datagenerating models have not been developed. In this study, we give a method forestimating the average causal effect for intervention in continuous variablesthat can be applied to data of any generating models as long as the causaleffect is identifiable. The proposing method is independent of machine learningalgorithms and preserves the identifiability of data.

Estimating the average causal effect of intervention in continuous variables using machine learning