Causation
Readings on causation as it applies to data science, and more specifically data science ethics. A primary focus of these readings is causal inference and socially sensitive attributes (e.g., race and gender).
Interactions with World
Ordering Data as Models to Represent the World
Interpreting Models as Knowledge
| Title | Citation |
|---|---|
| The Metaphysics of Causation (Stanford Encyclopedia of Philosophy) | Scheines (n.d.) |
| The Problem of Induction (Stanford Encyclopedia of Philosophy) | Henderson (2022) |
| Causation | Scheines (n.d.) |
| Causal Diagrams for Empirical Research | Pearl (1995) |
| Disparate Causes, Pt. I | Hu (2019) |
| Variation Semantics: When Counterfactuals in Explanations of Algorithmic Decisions are True | Hudetz & Crawford (2022) |
| Causal Discovery Algorithms: A Practical Guide | Malinsky & Danks (2018) |
| On the Explanatory Depth and Pragmatic Value of Coarse-Grained, Probabilistic, Causal Explanations | Kinney (2018) |
| Fairness in Decision-making — the Causal Explanation Formula | (Zhang2018?) |
| Evaluations of Causal Claims Reflect a Trade-Off Between Informativeness and Compression | Kinney & Lombrozo (2022) |
| The Use and Misuse of Counterfactuals in Ethical Machine Learning | Kasirzadeh & Smart (2021) |
| Eddie Murphy and the Dangers of Counterfactual Causal Thinking About Detecting Racial Discrimination | Kohler-Hausmann (2017) |
| "But What Are You Really?": The Metaphysics of Race | Mills (1998) |
| What is "Race" in Algorithmic Discrimination on the Basis of Race? | Hu (Forthcoming) |
References
Henderson, L. (2022). The Problem of Induction. In E. N. Zalta & U. Nodelman (Eds.), The Stanford encyclopedia of philosophy (Winter 2022). https://plato.stanford.edu/archives/win2022/entries/induction-problem/; Metaphysics Research Lab, Stanford University.
Hu, L. (2019). Disparate causes, pt. i. Retrieved from https://www.phenomenalworld.org/analysis/disparate-causes-i/
Hu, L. (Forthcoming). What’s ’race’ in algorithmic discrimination on the basis of race? Journal of Moral Philosophy.
Hudetz, L., & Crawford, N. (2022). Variation semantics: When counterfactuals in explanations of algorithmic decisions are true. Retrieved from https://philsci-archive.pitt.edu/20626/
Kasirzadeh, A., & Smart, A. (2021). The use and misuse of counterfactuals in ethical machine learning. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 228–236. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3442188.3445886
Kinney, D. (2018). On the explanatory depth and pragmatic value of coarse-grained, probabilistic, causal explanations. Philosophy of Science, (1), 145–167. https://doi.org/10.1086/701072
Kinney, D., & Lombrozo, T. (2022). Evaluations of causal claims reflect a trade-off between informativeness and compression. Annual Meeting of the Cognitive Science Society. Retrieved from https://api.semanticscholar.org/CorpusID:269447691
Kohler-Hausmann, I. (2017). The dangers of counterfactual causal thinking about detecting racial discrimination. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3050650
Malinsky, D., & Danks, D. (2018). Causal discovery algorithms: A practical guide. Philosophy Compass, 13(1), e12470. https://doi.org/10.1111/phc3.12470
Mills, C. W. (1998). "But what are you really?": The metaphysics of race. In C. W. Mills (Ed.), Blackness visible: Essays on philosophy and race (pp. 41–66). Cornell University Press.
Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82, 669–688. Retrieved from https://api.semanticscholar.org/CorpusID:10023329
Scheines, R. (n.d.). Causation. Retrieved from https://www.cmu.edu/dietrich/philosophy/docs/scheines/causation.pdf