Causal Models for Business, Economic, And Social Engineering

Abstract

Causal models offer a robust framework for facilitating corporate restructuring and innovation. This paper discusses how these models can evolve and serve as critical tools in business engineering, enabling management to envision the desired future state of a firm and the path to achieve it. The evolution of causal models parallels paradigm shifts in scientific fields, requiring changes not only in structural equations and causal graphs but also in the descriptive frameworks of firms. This often results in firms that are "incommensurable" with their previous states, requiring the construction of new performance metrics. Causal models, defined as sets of mathematical relationships between variables, help in identifying and implementing interventions that drive innovation. They support management in creating detailed, conceptual images of a firm's internal workings, facilitating the design and execution of change processes. By constructing and updating causal models through the innovation journey, management can better anticipate and manage the complexities and uncertainties involved. Ultimately, a deep innovation process often leads to a paradigm shift, changing the firm's variables and business concepts. This shift can be challenging to communicate and compare with the past, but it is essential for achieving sustainable and transformative change.

Keywords: causal models, paradigm shift, structural equation models, intervention, business engineering, self-organizing and guiding system changes