The ecosystem services argument is a highly publicised instrumental argument for protecting biodiversity. I develop a new objection to this argument based on the lack of a causal connection from global species losses to local ecosystem changes. I survey some alternative formulations of services arguments, including ones incorporating option value or a precautionary principle, and show that they do not fare much better than the standard version. I conclude that environmental thinkers should rely less on ecosystem services as a means (...) to defend biodiversity, and that attention should be focused on additional types of value which might be attributed to global biodiversity. (shrink)

In the biomedical, behavioural and social sciences, the leading method used to estimate causal effects is commonly randomised controlled trials (RCTs) that are generally viewed as both the source and justification of the most valid evidence. In studying the foundation and theory behind RCTs, the existing literature analyses important single issues and biases in isolation that influence causal outcomes in trials (such as randomisation, statistical probabilities and placebos). The common account of biased causal inference is described in a general way (...) in terms of probabilistic imbalances between trial groups. This paper expands the common account of causal bias by distinguishing between the range of biases arising between trial groups but also within one of the groups or across the entire sample during trial design, implementation and analysis. This is done by providing concrete examples from highly influential RCT studies. In going beyond the existing RCT literature, the paper provides a broader, practice-based account of causal bias that specifies the between-group, within-group and across-group biases that affect the estimated causal results of trials – impacting both the effect size and statistical significance. Within this expanded framework, we can better identify the range of different types of biases we face in practice and address the central question about the overall validity of the RCT method and its causal claims. A study can face several smaller biases (related simultaneously to a smaller sample, smaller estimated effect, greater unblinding etc.) that generally add up to greater aggregate bias. Though difficult to measure precisely, it is important to assess and provide information in studies on how much different sources of bias, combined, can explain the estimated causal effect. The RCT method is thereby often the best we have to inform our policy decisions – and the evidence is strengthened when combined with multiple studies and other methods. Yet there is room for continually improving trials and identifying ways to reduce biases they face and to increase their overall validity. Implications are discussed. (shrink)

Extrapolating causal effects is becoming an increasingly important kind of inference in Evidence-Based Policy, development economics, and microeconometrics more generally. While several strategies have been proposed to aid with extrapolation, the existing methodological literature has left our understanding of what extrapolation consists of and what constitutes successful extrapolation underdeveloped. This paper addresses this lack in understanding by offering a novel account of successful extrapolation. Building on existing contributions pertaining to the challenges involved in extrapolation, this more nuanced and comprehensive account (...) seeks to provide tools that facilitate the scrutiny of specific extrapolative inferences and general strategies for extrapolation. Offering such resources is important especially in view of the increasing amounts of real-world decision-making in policy, development, and beyond that involve extrapolation. (shrink)

Public policymakers and institutional decision-makers routinely face questions about whether interventions “work”: does universal basic income improve people’s welfare and stimulate entrepreneurial activity? Do gated alleyways reduce burglaries or merely shift the crime burden to neighbouring communities? What is the most cost-effective way to improve students’ reading abilities? These are empirical questions that seem best answered by looking at the world, rather than trusting speculations about what will be effective. Evidence-based policy (EBP) is a movement that concretizes this intuition. It (...) maintains that policy should be based on evidence of “what works.” Not any evidence will do, however. Following on the heels of its intellectual progenitor, evidence-based medicine (EBM), EBP insists on the use of high-quality evidence produced in accordance with rigorous methodological standards. Though intuitively compelling, EBP has attracted significant criticism from methodologists, political scientists, and philosophers. This chapter provides a critical overview of EBP through a philosophical lens, reviewing and discussing some of the most pressing challenges that EBP faces and outlining some proposals for improving it. I first provide a brief overview of EBP and distinguish a broader and narrower understanding of it. I then review existing lines of criticism of EBP, two of which are considered in detail. The first elaborates how EBP struggles with extrapolation, that is, the use of evidence from study populations to make inferences about the effects of policies on novel target populations. The second maintains that EBP’s methodological tenets are deeply entwined with moral and political values, which can threaten EBP’s promise to promote objectivity in policymaking. Finally, I make some proposals for how EBP could be improved going forward. (shrink)