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Throughout the Covid-19 pandemic, people have been eager to learn what factors, and especially what public health policies, cause infection rates to wax and wane. But figuring out conclusively what causes what is difficult in complex systems with nonlinear dynamics, such as pandemics. We review some of the challenges that scientists have faced in answering quantitative causal questions during the Covid-19 pandemic, and suggest that these challenges are a reason to augment the moral dimension of conversations about causal inference. We take a lesson from Martha Nussbaum—who cautions us not to think we have just one question on our hands when we have at least two—and apply it to modeling for causal inference in the context of cost-benefit analysis.

Both the distinction between the 'internal' and 'external' phases of science and the concept of 'inductive risk' are core constructs in the values in science literature. However, both constructs have shortcomings, which, we argue, have concealed the unique significance of values in scientific representation. We defend three closely-related proposals to rectify the problem: i) to draw a conceptual distinction between endorsing a 'fact' and making a decision about representation; ii) to employ a conception of inductive risk that aligns with this distinction, not one between internal/external phases in science; iii) to conceptualize 'representational risk' as a unique epistemic risk, no less significant than inductive risk. We outline the implications of each proposal for current debates in the values in science literature.

Both the distinction between the 'internal' and 'external' phases of science and the concept of 'inductive risk' are core constructs in the values in science literature. However, both constructs have shortcomings, which, we argue, have concealed the unique significance of values in scientific representation. We defend three closely-related proposals to rectify the problem: i) to draw a conceptual distinction between adopting a 'fact' and making a decision about representation; ii) to employ a conception of inductive risk that aligns with this distinction, not one between internal/external phases in science; iii) to conceptualize 'representational risk' as a unique epistemic risk, no less significant than inductive risk. We outline the implications of each proposal for current debates in the values in science literature.

The argument from inductive risk (AIR) is an important challenge to the Value-Free Ideal; yet philosophers disagree over how to define it. We argue that the AIR that best aligns with Rudner's (1953) and Hempel's (1954, 1965) views is the one that describes the risk scientists take in invoking 'facts'. Further, we argue that representational decisions in science necessarily invoke both facts and non-epistemic values and should not be equated with invoking facts; therefore, the AIR should not be applied to representational decisions. Last, we define a 'representational risk', showing how this helps clarify the role of values in science.