Within the causal modeling literature, debates about the Causal Faithfulness Condition (CFC) have concerned whether it is probable that the parameters in causal models will have values such that distinct causal paths will cancel. As the parameters in a model are fixed by the probability distribution over its variables, it is initially puzzling what it means to assign probabilities to these parameters. I propose that to assign a probability to a parameter in a model is to treat that parameter as a function of a variable in an augmented model. By combining this proposal with widely adopted principles regarding which variables must be included in a model, I argue that the various proposed counterexamples to CFC involving coordinated parameters are not genuine counterexamples. I then consider the cases in which CFC fails due not to coordination, but by coincidence, and propose explanatory and predictive bases for ruling out such coincidences without presuming that they are improbable. The aim of the proposed defenses is not to show that CFC never fails, but rather to argue that its use in a particular context may be defended using general modeling assumptions rather than by relying on claims about how often it fails.

Borsboom, Mellenbergh, and van Heerden argue that latent variables such as intelligence should be given a between-subjects causal interpretation, but not a within-subjects causal interpretation. That is, while intelligence is a cause of one subject’s doing better than another on an IQ test, there is no non-comparative sense in which intelligence – as standardly measured – is a cause of an individual’s performance. Here I expand upon Pearl’s discussion of Simpson’s paradox to show that there cannot be a cause in a population that is not a cause in at least one of its members and that, consequently, causal variables cannot have an exclusively between-subjects interpretation. The illusion that they can results from not properly distinguishing between causal and non-causal models.

In their 2010 book, Biology's First Law, D. McShea and R. Brandon present a principle that they call ''ZFEL,'' the zero force evolutionary law. ZFEL says (roughly) that when there are no evolutionary forces acting on a population, the population's complexity (i.e., how diverse its member organisms are) will increase.Here we develop criticisms of ZFEL and describe a different law of evolution; it says that diversity and complexity do not change when there are no evolutionary causes.

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A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter time-scales. Dynamic causal models (Iwasaki and Simon, 1994) explain how doing so reveals a system's non-equilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a time-scale. The models reveal that key features of causal representations -such as which variables are exogenous -may vary with the time-scale at which a system is considered. This has implications for debates regarding which systems can be understood causally.