Summer 2019 - PHIL 864 G100

Directed Studies: Selected Topics IV (3)

Causation and Explanation

Class Number: 6063

Delivery Method: In Person


  • Course Times + Location:

    Location: TBA



Directed Reading: Causal & Noncausal Explanation

Important note regarding enrollment: All seats are reserved for Philosophy Graduate students. Permission from instructor is required to join this course. Please forward permission to the Graduate Assistant for enrollment into the course. 

Overview: This 3 credit course will continue the work of an existing reading group that has run through the Spring 2019 term without credit. It involves a high degree of prior knowledge of discussions in philosophy of science related to or involving causation and explanation.

The readings are selected to investigate the topic of causation and explanation from the perspective of distinguishing causal from various kinds of non-causal explanation. This is accomplished by looking at accounts of explanation, accounts of causation, and of causal explanation. By the end of this course, students will have a comprehensive background in contemporary debates about causal and noncausal explanation, and be well positioned to contribute original work to these debates.


  • Semi-weekly presentations of readings 50%
  • A polished term paper revised in light of feedback 50%



All material will be available on Canvas


Readings list: Readings will be drawn from this list. Using student presentations on targeted papers, the group will average 2 readings per week.

Causal Account of Explanation

• Lewis, D. (1986). Causal explanation. In Philosophical Papers, vol. II (New York: Oxford University Press), 214–40.

• Woodward, J. (2003). Making things happen: A theory of causal explanation. OUP.

• *Strevens, M. (2004). The Causal and Unification Approaches to Explanation Unified‐Causally. Noûs, 38(1), 154–176.

• Craver, C. (2006) ‘When Mechanistic Models Explain,’ Synthese, 153(3), 355–376.

• *Pendergraft, G. (2011). In defense of a causal requirement on explanation. In Causality in the Sciences, 470.

• Andersen, H. (2017) Patterns, Information, and Causation. Journal of Philosophy 114 (11), 592-622

Non-causal Explanation: General Accounts

• Reutlinger, A. (2017): Explanation Beyond Causation? New Directions in the Philosophy of Scientific Explanation. Philosophy Compass.

• Baker, A. (2009). Mathematical Explanations in Science. The British Journal for the Philosophy of Science, 60, 611–633.

• Skow, B. (2013). Are There Non-Causal Explanations (of Particular Events)? British Journal for the Philosophy of Science.

• Pincock, C. (2015). Abstract Explanations in Science. British Journal for the Philosophy of Science 66: 857–82.

• Batterman, R. (2010) On the Explanatory Role of Mathematics in Empirical Science. The British Journal for the Philosophy of Science, 61, 1–25.

• Lange, M. (2018). Because Without Cause Scientific Explanations by Constraint. In Explanation Beyond Causation: Philosophical Perspectives on Non-Causal Explanations.

• Lange, M. (2013). What makes a scientific explanation distinctively mathematical? The British Journal for the Philosophy of Science, 64, 485–511.

• Pincock, C. (2018). Accommodating explanatory pluralism. In Explanation beyond causation.

Non-causal Explanation: Cases Studies

• Morrison (2018), The Non-Causal Character of Renormalization Group Explanations. In Explanation beyond causation.

• Pexton, M. (2014). How dimensional analysis can explain. Synthese, 191(10), 2333–2351.

• Bokulich, A. (2018). Searching for non‐causal explanations in a sea of causes. In Explanation beyond causation.

• Rice, C. (2015). Moving beyond causes: Optimality models and scientific explanation. Nous, 49, 589–615.

• Ariew, A., Rice, C., & Rohwer, Y. (2014). Autonomous-statistical explanations and natural selection. British Journal for the Philosophy of Science, 66(3), 635-658.

• Sarkar, S. (2011). Drift and the Causes of Evolution. In Causality in the Sciences.

• Huneman, P. (2010). Topological explanations and robustness in biological sciences. Synthese, 177, 213–245.

• Jones, N. (2018). Strategies of Explanatory Abstraction in Molecular Systems Biology. Philosophy of Science, 85(5), 955-968.

• Irvine, E. (2015). Models, robustness, and non-causal explanation: a foray into cognitive science and biology. Synthese, 192(12), 3943-3959.

• Ross, L. (2015). Dynamical Models and Explanation in Neuroscience. Philosophy of Science 82: 32–54.

• Chirimuuta, M. (2017). Explanation in computational neuroscience: Causal and non-causal. The British Journal for the Philosophy of Science, 69(3), 849-880.

Causal vs. Non-causal

• Andersen, H. (2016). Complements, not competitors: Causal and mathematical explanations. The British Journal for the Philosophy of Science.

• Strevens, M. (2018). The mathematical route to causal understanding. In Explanation beyond causation.

• Woodward, J. (2018). Some Varieties of Non‐Causal Explanation. In Explanation beyond causation.

• Reutlinger, A., & Andersen, H. (2016). Abstract versus Causal Explanations?. International Studies in the Philosophy of Science, 30(2), 129-146.

• Reutlinger, A. (2018). Extending the Counterfactual Theory of Explanation. In Explanation beyond causation.

• Kaplan, D. and Craver, C. (2011), ‘The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective’, Philosophy of Science 78: 601–27.

• Jansson, L. (2018). When are Structural Equation Models Apt? Causation versus Grounding. In Explanation beyond causation.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

SFU’s Academic Integrity web site is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University.