Evaluation of the Causal Effect of Control Plans in Nonrecursive Structural Equation Models
This work addresses a specific methodological issue in causal inference for control planning in nonrecursive systems, representing an incremental advancement in the field.
The authors tackled the problem of evaluating the causal effect of control plans on response variables using observational data and known directed cyclic graphs, formulating an optimal control plan in linear nonrecursive structural equation models and clarifying its properties to enable variance effect evaluation.
When observational data is available from practical studies and a directed cyclic graph for how various variables affect each other is known based on substantive understanding of the process, we consider a problem in which a control plan of a treatment variable is conducted in order to bring a response variable close to a target value with variation reduction. We formulate an optimal control plan concerning a certain treatment variable through path coefficients in the framework of linear nonrecursive structural equation models. Based on the formulation, we clarify the properties of causal effects when conducting a control plan. The results enable us to evaluate the effect of a control plan on the variance from observational data.