 One relatively secure way to determine causal relations is via randomized controlled experiments. If the experimenters sort subjects randomly into experimental and control groups and vary just one factor, then, unless by bad luck the two groups differ in some unknown way, changes in the outcomes given the common features of the control and treatment groups should be due to the difference in the one factor. Indeed, in the case of quantitative variables, one can calculate average causal effects (Deaton 2010). This makes randomized controlled trials very attractive, though no panacea, since the treatment and control groups may not be representative of the population in which policy-makers hope to apply the causal conclusions, and the causal consequences of the intervention might differ across different subgroups within the control and treatment groups (Worrall 2007, Cartwright and Hardie 2013).