Structural Causal Models in Strategy: Opportunities and Boundaries

Causality is at the center of all scientific endeavors. From prior research, we know that just like scientists, business leaders, too, make causal assumptions about their environment to guide strategic choices. Causal machine learning has consequently been subject to growing attention in strategic management research and practice. While causal AI promises significant opportunities for improved data-driven decisions about strategy and strategic action, its successful application to strategy seems far from trivial. In this text, we explore how causal AI can be incorporate into firms' strategizing and analyze opportunities and boundaries of such application. We submit Structural Causal Models as a framework for managerial theorizing and find that careful application can address strategic challenges of fairness, explainable and robustness in AI which present an important obstacle to data-driven decisions. We conclude with a discussion of the boundaries of causal analysis in strategy.