Causal reasoning and inference are at the core of value-generating theories of strategic decision-makers. Based on research in human cognition and arguments on strategists’ reliance on visuals, we suggest structural causal modeling and particularly its graphical component, causal graphs (e.g., DAG) as a useful representation of decision makers’ causal theories. We present the results of three experiments which show that causal assumptions of a theory are difficult to reconcile with probabilistic reasoning, and that causal diagrams can help bridge this gap. Key factors for the usefulness of causal graphs for strategic decision-making are task complexity and experience in causal modeling. We discuss the implications of our findings for theories of the theory-based view and strategic decision-making more generally, and suggest future research questions that arise from this study.
Ansprechpartner: Jordan Bisset
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