Grounding is often regarded as a kind of metaphysical causation (Wilson 2018). Some even consider causation as a particular grounding (or building) relation (Bennett 2017). However, although there are strong similarities in content, structure, and form, the analogy is not uncontroversial given some notable differences (Bernstein 2016). In this talk, I argue for a restricted analogy that does justice to both the striking similarities and the key differences. Specifically, I argue that the analogy holds only between causation and particular grounding relations, namely those that connect events on different levels of nature (or facts about such events). I show that this refined analogy overcomes in particular the problems of missing grounding analogs for productive, negative, and probabilistic causation.

Craver (2007) has argued that constitutive relevance can be discovered by mutual manipulability, based on interventions. However, the requirements on interventions make mutual manipulability of mechanisms and their constituents impossible.

Craver, Glennan, and Povich’s (2021) thesis that constitutive relevance can be reduced to “causal betweenness”, between the input and the output condition of a mechanism, is not adequate for multi-level mechanisms. Their account of the construction of models for mechanisms leads to the paradoxical result that there are no levels and no multi-level mechanisms, but only causal chains of fundamental level activities.

Models of multi-level mechanisms can be constructed on the basis of empirical information although the relevant experiments directly provide only information about causal relations, because this causal information can bear on variables at different levels. A multi-level model is built in two steps. 1) first, partial purely causal models are built for each hypothetical constituent variable i, on the basis of top-down and bottom-up experiments that modify or measure i in a level-specific way, 2) second, those partial models are merged in a comprehensive model containing both causal and constitution relations between variables, on the basis of information about the level of each variable and spatio-temporal constraints.

In a number of papers, Marc Lange and others have argued that statistical theorems non-causally explain empirical matters of fact. A typical (toy-)example would be an explanation of the average outcome of throwing dice in terms of the law of large numbers. Lange calls those explanations Really Statistical Explanations (RS Explanations). In my talk, I will argue that viewing RS explanations as non-causal is mistaken and misrepresents their function. I will show that in spite of the fact that information about the explanandum’s causal history is not explicitly mentioned when stating RS explanations, such information is pragmatically conveyed due to features of the conversational context. Next, I will argue that the thus conveyed information is essential to account for why statistical theorems explain empirical matters of fact in the first place. In the final part of the talk, I sketch a novel interpretation of RS explanations as a particular kind of coarse-graining explanations.

Some scientific knowledge involves knowing how to make something. In this paper we build on Currie’s recent examination of this kind of “maker’s knowledge” in experimental archaeology (Currie 2022). This examination leads to two important extensions of Currie’s proposal. First, we show how Currie’s proposal helps to make sense of another debate in experimental archaeology. This debate involves figuring out how the moai (statues) of Rapa Nui (Easter Island) were moved to their intended places on the island. Second, unlike Currie, we explore how maker’s knowledge is best connected with competing accounts of knowing how. We argue that knowing how to make something is its own kind of cognitive achievement that should not be treated as a species of knowing how to do something and that it is intimately related to an objectual understanding of the thing to be made.