This paper addresses the question of what constitutes an apt interpreted model for the purpose of analyzing causation. I first collect universally adopted aptness principles into a basic account, flagging open questions and choice points along the way. I then explore various additional aptness principles that have been proposed in the literature but have not been widely adopted, the motivations behind their proposals, and the concerns with each that stand in the way of universal adoption. I conclude that the remaining (...) work of articulating aptness for a SEM analysis of causation is tied up with issues to do with modality, ontology, and mereology. Continuing this work is therefore likely to shed light on the relationship between these areas and causation more generally. (shrink)

A key challenge for probabilistic causal models is to distinguish non-causal probabilistic dependencies from true causal relations. To accomplish this task, causal models are usually required to satisfy several constraints. Two prominent constraints are the causal Markov condition and the faithfulness condition. However, other constraints are also needed. One of these additional constraints is the causal sufficiency condition, which states that models must not omit any direct common causes of the variables they contain. In this paper, I argue that the (...) causal sufficiency condition is problematic: (1) it is incompatible with the requirement that the variables in a model must not stand in non-causal necessary dependence relations, such as mathematical or conceptual relations, or relations described in terms of supervenience or grounding, (2) it presupposes more causal knowledge as primitive than is actually needed to create adequate causal models, and (3) if models are only required to be causally sufficient, they cannot deal with cases where variables are probabilistically related by accident, such as Sober’s example of the relationship between bread prices in England and the sea level in Venice. I show that these problems can be avoided if causal models are required to be monotonic in the following sense: the causal relations occurring in a model M would not disappear if further variables were added to M. I give a definition of this monotonicity condition and conclude that causal models should be required to be monotonic rather than causally sufficient. (shrink)

A promising development in the philosophy of causation analyzes actual causation using structural equation models, i.e., “causal models”. This paper carefully considers what it means for an interpreted model to be accurate of its target situation. These considerations show, first, that our existing understanding of accuracy is inadequate. Further, and more controversially, they show that any causal model analysis is committed to a kind of relativism – a view whereby causation is a three-part relation holding between a cause, an effect, (...) and something else. In particular, insofar as a causal model analysis construes causation mind-and-language independently, it must treat causation as relative to a specification of background possibilities – i.e., a ‘modal profile.’ Or, so I argue. (shrink)

Why are quantum correlations so puzzling? A standard answer is that they seem to require either nonlocal influences or conspiratorial coincidences. This suggests that by embracing nonlocal influences we can avoid conspiratorial fine-tuning. But that’s not entirely true. Recent work, leveraging the framework of graphical causal models, shows that even with nonlocal influences, a kind of fine-tuning is needed to recover quantum correlations. This fine-tuning arises because the world has to be just so as to disable the use of nonlocal (...) influences to signal, as required by the no-signaling theorem. This places an extra burden on theories that posit nonlocal influences, such as Bohmian mechanics, of explaining why such influences are inaccessible to causal control. I argue that Everettian Quantum Mechanics suffers no such burden. Not only does it not posit nonlocal influences, it operates outside the causal models framework that was presupposed in raising the fine-tuning worry. Specifically, it represents subsystems with density matrices instead of random variables. This allows it to sidestep all the results (including EPR and Bell) that put quantum correlations in tension with causal models. However, this doesn’t mean one must abandon causal reasoning altogether in a quantum world. When decoherence is rampant and there’s no controlled entanglement, Everettian Quantum Mechanics licenses our continued use of standard causal models. When controlled entanglement is present---such as in Bell-type experiments---we can employ recently-proposed quantum causal models that are consistent with Everettian Quantum Mechanics. We never need invoke any kind of non-local influence or any kind of fine-tuning. (shrink)

A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from Bayesian networks, information theory, and decision theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness, where the optimal trade-off depends on the decision-theoretic value of information for a given agent in a given context. This theory predicts that, all (...) else being equal, agents prefer causal models that are as compressed as possible. When compression is associated with information loss, however, all else is not equal, and our theory predicts that agents will favor compressed models only when the information they sacrifice is not informative with respect to the agent’s anticipated decisions. We then show, across six studies reported here (N=2,364) and one study reported in the supplemental materials (N=182), that participants’ preferences over causal models are in keeping with the predictions of our theory. Our theory offers a unification of different dimensions of causal evaluation identified within the philosophy of science (proportionality and stability), and contributes to a more general picture of human cognition according to which the capacity to create compressed (causal) representations plays a central role. (shrink)

Peter Van Inwagen argues that The Principle of Sufficient Reason (PSR) either leads to contradiction or it leads to necessitarianism. Although I agree with Van Inwagen that the relationship between the PSR and necessitarianism is close, I argue that the PSR is compatible with innocent versions of necessitarianism. In this project my main argument is that modal realism can account for the PSR and integrate it within an innocent version of necessitarianism. My main claim will be that each fact in (...) each possible world is grounded in the principle of plenitude. Also, while each fact is grounded in plenitude, it avoids rigid necessitarianism because (i) actuality is not captured by the PSR, (ii) things could be otherwise because there are other worlds, and due to the principle of recombination (iii) cardinality of possible worlds is maximized and (iv) there are no necessary connections between distinct individuals. As a result, the relationship between modal realism and the PSR is symbiotic. Through modal realism, the PSR also results as: having its scope extended to the realm of all possibilia rather than being confined to mere actuality; it solves indeterminacy problems like Leibniz’s problem of space or the hard problem of consciousness; it accounts for facts in lawless worlds etc. On the other hand, through the PSR, modal realism gets one more benefit in its inventory because it accounts for the PSR. Thus, modal realism still continues on Lewis’ paradoxical path of proving to be highly pragmatic but counterintuitive at the same time. If added benefits are worth the costs for modal realism is still an open question. (shrink)

This paper argues that the knowledge asymmetry (the fact that we know more about the past than the future) can be explained as a consequence of the causal Markov condition. The causal Markov condition implies that causes of a common effect are generally statistically independent, whereas effects of a common cause are generally correlated. I show that together with certain facts about the physics of our world, the statistical independence of causes severely limits our ability to predict the future, whereas (...) correlations between joint effects make it so that no such limitation holds in the reverse temporal direction. In so far as the fact that our world conforms to the causal Markov condition can itself be explained in terms of the initial conditions of the universe, my view is compatible with Albert’s well-known account of the origins of temporal asymmetries, but also provides a more illuminating way to derive the knowledge asymmetry from those initial conditions. (shrink)