This article provides a theory of causation in the causal modeling framework. In contrast to most of its predecessors, this theory is model-invariant in the following sense: if the theory says that C caused (didn’t cause) E in a causal model, M, then it will continue to say that that C caused (didn’t cause) E once one has removed an inessential variable from M. The article suggests that, if this theory is true, then one should understand a cause as something which transmits deviant or noninertial behavior to its effect.

Weisberg (2009) provides an argument that neither conditionalization nor Jeffrey conditionalization is capable of accommodating the holist's claim that beliefs acquired directly from experience can suffer undercutting defeat. I diagnose this failure as stemming from the fact that neither conditionalization nor Jeffrey conditionalization give any advice about how to rationally respond to theorydependent evidence, and I propose a novel updating procedure which does tell us how to respond to evidence like this. is holistic updating rule yields conditionalization as a special case in which our evidence is entirely theory-independent.

The externalist says that your evidence could fail to tell you what evidence you do or not do have. In that case, it could be rational for you to be uncertain about what your evidence is. This is a kind of uncertainty which orthodox Bayesian epistemology has difficulty modeling. For, if externalism is correct, then the orthodox Bayesian learning norms of conditionalization and reflection are inconsistent with each other. I recommend that an externalist Bayesian reject conditionalization. In its stead, I provide a new theory of rational learning for the externalist. I defend this theory by arguing that its advice will be followed by anyone whose learning dispositions maximize expected accuracy. I then explore some of this theory's consequences for the rationality of epistemic akrasia, peer disagreement, undercutting defeat, and uncertain evidence.

I present an account of deterministic chance which builds upon the physico-mathematical approach to theorizing about deterministic chance known as the method of arbitrary functions. This approach promisingly yields deterministic probabilities which align with what we take the chances to be-it tells us that there is approximately a 1/2 probability of a spun roulette wheel stopping on black, and approximately a 1/2 probability of a flipped coin landing heads up-but it requires some probabilistic materials to work with. I contend that the right probabilistic materials are found in reasonable initial credence distributions. I note that, with some rather weak normative assumptions, the resulting account entails that deterministic chances obey a variant of Lewis's 'principal principle'. I additionally argue that deterministic chances, so understood, are capable of explaining long-run frequencies.

While structural equations modeling is increasingly used in philosophical theorizing about causation, it remains unclear what it takes for a particular structural equations model to be correct. To the extent that this issue has been addressed, the consensus appears to be that it takes a certain family of causal counterfactuals being true. I argue that this account faces difficulties in securing the independent manipulability of the structural determination relations represented in a correct structural equations model. I then offer an alternate understanding of structural determination, and I demonstrate that this theory guarantees that structural determination relations are independently manipulable. The account provides a straightforward way of understanding hypothetical interventions, as well as a criterion for distinguishing hypothetical changes in the values of variables which constitute interventions from those which do not. It additionally affords a semantics for causal counterfactual conditionals which is able to yield a clean solution to a problem case for the standard 'closest possible world' semantics.