Jan Zebrowski

Model-based science—the style of theoretical work dominant in many social sciences, including economics—studies complex real-world systems indirectly through highly idealized model systems. The viability of model-based science pivots on the possibility of determining ‘difference-makers’ for every system it studies. However, economic systems are neither clearly circumscribed nor ‘closed’ in the sense that any outcome studied by economists is, to a greater or lesser extent, causally influenced by an infinitely complex network of factors. This makes salient the question: How do economists determine which factors are explanatorily relevant to any given outcome and should be included in its explanation? This is the problem of explanatory relevance. In this paper, I try to make headway towards solving this problem using robustness analysis (RA)—a well-known procedure in theoretical economics by which modellers gauge the sensitivity of their models' results to assumptions that fuel the derivation of these results.