Inference of Factors for Labor Productivity Growth Used Randomized Experiment and Statistical Causality

Abstract

The study of causal dependencies in economics is fraught with great difficulties, that it is required to consider not only the object structure, but also take into account a huge number of factors acting on the object, about which nothing is either known or difficult to measure. In this paper, we attempt to overcome this problem and apply the theory of statistical causality for labor productivity management. We suggest new technology that provides the inference of causal relations between the special programs implemented in the company’s and employee’s labor productivity. The novelty of the proposed technology is that it is based on a hybrid object model, combines two models: 1—the structural object model about its functioning and development to provide a causal inference and prediction the effect of explicit factors; 2—the model based on observed data to clarify causality and to test it empirically. The technology provides integration of the theory of causal Bayesian networks, methods of randomized controlled experiments and statistical methods, allows under nonlinearity, dynamism, stochasticity and non-stationarity of the initial data, to evaluate the effect of programs on the labor effeciency. The difference between the proposed technology and others is that it ensures determination the synergistic effect of the action of the cause (program) on the effect—labor productivity in condition of hidden factors. The practical significance of the research is the results of its testing the proposed theoretical provisions, methods and technologies on actual data about food service company. The results obtained could contribute to the labor productivity growth over uncertainty of the external and internal factors and provide the companies sustainable development and its profitability growth.