Purpose: The causality search Taguchi (CS-T) method was proposed to support system selection in a robust parameter design. However, the target of the analysis is likely to be quasi-experimental data. This can be difficult to analyse with the CS-T method. Therefore, this study proposes a new analysis approach that can perform a more accurate knowledge search by applying the instrumental variable. Methodology/Approach: Using the CS-T method, appropriate knowledge search is difficult with quasi-experimental data, including endogeneity. We examined an analytical process that addresses the endogeneity between mechanism and output by utilizing the control and noise factors that constitute the mechanism as instrumental variables. Findings: The results show that 1) the proposed method has sufficient practical accuracy, even for quasi-experimental data including endogeneity; and 2) the extracted mechanism is less likely to fluctuate depending on the number of experimental conditions used. Moreover, we can clarify the position of the CS-T and proposed methods in system selection. Research Limitation/Implication: We perform estimation under the assumption that the threshold is known. However, the extracted mechanism may change depending on the threshold; this requires discussing how to determine them. Originality/Value of paper: Technological development requires a high degree of engineer sophistication. However, this study’s analytical process allows conducting more accurate knowledge search in a realistic and systematic way without requiring a high level of engineer input.
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