Data Science Ethics Summer 2023Causation Readings
Additional Causation Readings
Lost in (Modal) Space: Demographic Base-Rate Neglect in the Service of Modal Knowledge
Evaluations of Causal Claims Reflect a Trade-Off Between Informativeness and Compression
Primary Causation Readings’ References
Halpern, J., & Hitchcock, C. (2011). Actual causation and the art of
modeling. Heuristics, Probability, and Causality.
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. (Forthcoming). What’s
’race’ in algorithmic discrimination on the
basis of race? Journal of Moral Philosophy.
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
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. (2017). Causal discovery algorithms: A
practical guide. Philosophy Compass, 13(1), e12470. https://doi.org/10.1111/phc3.12470
Munton, J. (2019). Beyond accuracy: Epistemic flaws with statistical
generalizations. Philosophical Issues, 29. https://doi.org/10.1111/phis.12150
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.