I first propose to distinguish between two kinds of external validity inferences, predictive and explanatory. I then argue that we have a satisfactory answer to the question of the conditions under which predictive external validity inferences are good. If this claim is correct, then it has two immediate consequences: First, some external validity inferences are deductive, contrary to what is commonly assumed. Second, Steel’s requirement that an account of external validity inference break what he calls the ‘Extrapolator’s Circle’ is misplaced, (...) at least when it comes to predictive external validity inferences. (shrink)

Advocates of the counterfactual approach to causal inference argue that race is not a cause, and this despite the fact that it is commonly treated as such by scientists in many disciplines. I object that their argument is unsound since two of its premises are false. I also sketch an argument to the effect that racial discrimination cannot be explained unless one assumes race to be a cause.

Policies to combat climate change should be supported by evidence regarding their effectiveness. But what kind of evidence is that? And what tools should one use to gather such evidence? Many argue that randomized controlled trials are the gold standard when it comes to evaluating the effects of policies. As a result, there has been a push for climate change policies to be evaluated using RCTs. We argue that this push is misguided. After explaining why RCTs are thought to be (...) the gold standard, we use examples of mitigation and adaptation policies to show that RCTs provide, at best, one piece of the evidential puzzle one needs to assemble for well-supported decisions regarding climate change policies. (shrink)