Exploratory Matching Model Search Algorithm (EMMSA) for Causal Analysis: Application to the Cardboard Industry

Abstract

This paper aims to present a methodology for the application of matching methods in industry to measure causal effect size. Matching methods allow us to obtain treatment and control samples with their covariates as similar as possible. The matching techniques used are nearest, optimal, full, coarsened exact matching (CEM), and genetic. These methods have been widely used in medical, psychological, and economic sciences. The proposed methodology provides two algorithms to execute these methods and to conduct an exhaustive search for the best models. It uses three conditions to ensure, as far as possible, the balance of all covariates, the maximum number of units in the treatment and control groups, and the most significant causal effect sizes. These techniques are applied in the carton board industry, where the causal variable is downtime, and the outcome variable is waste generated. A dataset from the carton board industry is used, and the results are contrasted with an expert in this process. Meta-analysis techniques are used to integrate the results of different comparative studies, which could help to determine and prioritize where to reduce waste. Two machines were found to generate more waste in terms of standardized measures whose values are 0.52 and 0.53, representing 48.60 and 36.79 linear meters (LM) on average for each production order with a total downtime of more than 3000 s. In general, for all machines, the maximum average wastage for each production order is 24.98 LM and its confidence interval is [13.40;36.23] LM. The main contribution of this work is the use of causal methodology to estimate the effect of downtime on waste in an industry. Particularly relevant is the contribution of an algorithm that aims to obtain the best matching model for this application. Its advantages and disadvantages are evaluated, and future areas of research are outlined. We believe that this methodology can be applied to other industries and fields of knowledge.