Causal models provide a promising framework for analyzing actual causation. Such analyses must include how a model should map onto the world. While universally endorsed that a model must be accurate – saying only true things – the implications of this aren’t explored. I argue that, surprisingly, accuracy is not had by a model tout court, but only relative to a space of possibilities. This discovery raises a problem for extant causal model theories and, indeed, for any theory of actual causation in terms of counterfactual or type-level causal dependence. I conclude with a view that resolves this problem.

Philosophical discussions of causal faithfulness have been predominantly situated within the social sciences—the traditional domain of application of the causal modeling techniques it attends. Recently, there has been increasing interest in applying such techniques to uncover causal relationships in biological systems. In this paper, I consider the extent to which faithfulness is a reasonable assumption in biological contexts and the problems that may results from relying on techniques that assume it. This discussion illuminates not only issues that may arise in causal modeling in biology, but also issues more generally relevant to understanding causal complexity in biological systems.

In discussions of the power of causal explanations, one often finds a commitment to two premises. The first is that, all else being equal, a causal explanation is powerful to the extent that it cites the full causal history of why the effect occurred. The second is that, all else being equal, causal explanations are powerful to the extent that the occurrence of a cause allows us to predict the occurrence of its effect. This article proves a representation theorem showing that there is a unique family of functions measuring a causal explanation's power that satisfies these two premises.

This paper elucidates an important feature of type-level causal relationships that is critical for understanding why disasters occur in sociotechnical systems. Using an interventionist theory, the paper explicates a concept, causal delay, to characterize differences between how rapidly or slowly interventions can make a difference to their effects. The paper then uses this explication to illuminate aspects of causal reasoning in everyday and scientific cases involving speed of change. In particular, the paper shows how causal delay clarifies why some systems are more prone to disasters than others. The paper closes by analyzing critical tradeoffs in choices between interventions.