Causation

Benjamin Eva (University of Konstanz), Stephan Hartmann (LMU Munich), Reuben Stern (LMU Munich)
Does y obtain under the counterfactual supposition that x? The answer to this question is famously thought to depend on whether y obtains in the most similar world(s) in which x obtains. What this notion of 'similarity' consists in is controversial, but in recent years, graphical causal models have proved incredibly useful in getting a handle on considerations of similarity between worlds. One limitation of the resulting conception of similarity is that it says nothing about what would obtain were the causal structure to be different from what it actually is, or from what we believe it to be. In this paper, we explore the possibility of using graphical causal models to resolve counterfactual queries about causal structure by introducing a notion of similarity between causal graphs. Since there are multiple principled senses in which a graph G* can be more similar to a graph G than a graph G**, we introduce multiple similarity metrics, as well as multiple ways to prioritize the various metrics when settling counterfactual queries about causal structure.

Peter Fazekas (University of Antwerp) Gergely Kertesz (Durham University)
This paper provides a detailed analysis and explores the prospects of the arguments for higher-level causal autonomy available for the proponents of the mechanistic framework. Three different arguments (a context-based, an organisation-based, and a constraint-based) are distinguished. After clarifying previously raised worries with regard to the first two arguments, the paper focuses on the newest version of the third argument that has recently been revived by William Bechtel. By using Bechtel's own case study, it is shown that not even reference to constraints can establish the causal autonomy of higher mechanistic levels.

Weixin Cai (Simon Fraser University)
There are two purposes of causal modeling. One is to predict which value an endogenous variable will take given that exogenous variables have some values, while the other is to explain why an endogenous variable takes a certain value. In this paper, I argue that to fulfill the second purpose, a model must capture the distinctive causal features of the causal structure it represents. This requires it to contain non-causally related variables.

Tomasz Wysocki (University of Pittsburgh)
In the paper, I propose an extension to accounts of token causation formulated in terms of structural equations models. First, I describe the target accounts. Then, I show how the case of circular causation that problems for these accounts, and I devise a way to revise them. On the standard approach, a graph representing the situation being modelled is acyclic, whereas the revised account allows for cycles. To illustrate my proposal, I present a macroeconomic model that is more naturally analyzed with a cyclic graph. Finally, I discuss the picture of causation entailed by my account.

Jennifer McDonald (The Graduate Center CUNY)
In this paper, I defend strong proportionality against what I take to be its principal objection — that proportionality fails to preserve common sense causal intuitions — by articulating independently plausible constraints on representing causal situations. I first assume the interventionist formulation of proportionality, following Woodward. This views proportionality as a relational constraint on variable selection in causal modeling that requires that changes in the cause variable line up with those in the effect variable. I then argue that the principal objection derives from a failure to recognize two constraints on variable selection presupposed by interventionism: exhaustivity and exclusivity.