PhilSci Archive

Strong Proportionality and Causal Claims

McDonald, Jennifer (2020) Strong Proportionality and Causal Claims. In: UNSPECIFIED.

This is the latest version of this item.

[img]
Preview
Text
McDonald.Strong Proportionality.pdf

Download (201kB) | Preview

Abstract

There are several supposedly lethal objections to the view that causation is essentially proportional. The first targets an account of proportionality in terms of causal models, pointing out that proportionality is too easily satisfied in causal model accounts of causation through manipulation of the range of values that a variable can take (Franklin-Hall, 2016). The second argues that proportionality legitimizes only the most general things as causes, and proportionality thereby contravenes causal intuitions (Bontly, 2005; Franklin-Hall, 2016; McDonnell, 2018, 2017; Weslake, 2013). The final, and perhaps most intractable, objection holds that proportionality counter-intuitively legitimizes disjunctive causes (Shapiro and Sober, 2012; Weslake, 2017; Woodward, 2018). This paper provides a unified response to these objections, which is best formulated in a causal model framework. I first articulate two independently plausible principles of variable selection – exclusivity and exhaustivity. I then show how the adoption of these principles responds to Franklin-Hall’s objection, and dissolves the remaining two.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
McDonald, Jenniferjenniferrosemcdonald@gmail.com
Keywords: proportional causation, causation, causal models, interventionism
Subjects: General Issues > Causation
General Issues > Models and Idealization
Depositing User: Ms Jennifer McDonald
Date Deposited: 14 Jan 2020 01:55
Last Modified: 14 Jan 2020 01:55
Item ID: 16809
Subjects: General Issues > Causation
General Issues > Models and Idealization
Date: 12 January 2020
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/16809

Available Versions of this Item

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item