I am a Lecturer in the Psychology Department at Yale University, where I teach classes in cognitive science and research methods, and a visiting researcher at Google Research, where I work on responsible AI and human-centered technology. Previously, I was a postdoctoral research associate at Princeton University, where I held a dual appointment in Tania Lombrozo's Concepts and Cognition Lab and the University Center for Human Values. Before that, I was an Omidyar Postdoctoral Fellow at the Santa Fe Institute, and before that, I completed my PhD in the Department of Philosophy, Logic and Scientific Method at the London School of Economics.
Most of my research concerns both intuitive and scientific modes of causal cognition. Causal theorizing is a distinctive way of thinking that is crucial to both the success of the sciences and the ordinary lives of agents. My research concerns normative and descriptive aspects of causal reasoning, with a specific focus on how humans select causal features to include in models of our environment. I also study the relationship between causal cognition and other cognitive processes like decision making, sociality, and morality in both human and artificial agents. In so doing, I use a mix of empirical, computational, and philosophical methods. I also have broad interests in formal epistemology, philosophy of science, and philosophy of probability. You can listen to me discuss several of my research projects on the Santa Fe Institute's Complexity Podcast. In addition to cognitive science and psychology, I have experience teaching college-level classes in digital humanities and philosophy.
I have also worked on consulting projects in artificial intelligence, decision science, and data science. In that capacity, I most recently served as Lead Decision Scientist at CareCentra, a healthcare technology and behavioral insights company.
Here is a copy of my CV. My e-mail address is david.kinney. @yale .eduPeer-Reviewed Journal Publications
- Forthcoming
-
Causal History, Statistical Relevance, and Explanatory Power. Philosophy of Science.
- 2023
Risk Aversion and Elite-Group Ignorance (with Liam Kofi Bright). Philosophy and Phenomenological Research. 106(1): 35-57Bayesian Networks and Causal Ecumenism. Erkenntnis. 88: 147–172- 2022
Why Average When You Can Stack? Better Methods for Generating Accurate Group Credences. Philosophy of Science. 89(4): 845-863Epistemology and Anomaly Detection in Astrobiology (with Christopher Kempes). Biology & Philosophy. 37:22Diachronic Trends in the Topic Distributions of Formal Epistemology Abstracts. Synthese. 200, 10.- 2021
Blocking an Argument for Emergent Chance. Journal of Philosophical Logic. 50: 1057–1077.Curie's Principle and Causal Graphs. Studies in History and Philosophy of Science. 87: 22-27.- 2019
On the Explanatory Depth and Pragmatic Value of Coarse-Grained, Probabilistic, Causal Explanations. Philosophy of Science. 86(1): 145-167.Inductive Explanation and Garber-Style Solutions to the Problem of Old Evidence. Synthese. 196(10): 3995-4009. Awarded the 2017/18 Popper Prize for Work by an LSE PhD Student.[Preprint][Journal Version (Open Access)]
Peer-Reviewed Publications in Conference Proceedings
- 2023
-
Show Me Your (Cogntive) Budget, and I'll Tell You What You Value: Evidential Relationships Between Values, Data, and Generic Causal Claims about the Social World (with Tania Lombrozo). Proceedings of the 45th Annual Meeting of the Cognitive Science Society.
- 2022
Evaluations of Causal Claims Reflect a Trade-Off Between Informativeness and Compression (with Tania Lombrozo). Proceedings of the 44th Annual Meeting of the Cognitive Science Society.- 2020
Causal Feature Learning for Utility-Maximizing Agents (with David Watson). Proceedings of Machine Learning Research (138): Tenth International Conference on Probabilistic Graphical Models (PGM).[Journal Version (Open Access)]
Invited Contributions and Book Reviews
- Forthcoming
-
Noisy Deductive Reasoning: How Humans Construct Math, and How Math Constructs Universes. (with David Wolpert). Undecidability, Uncomputability, and Unpredictability. Springer. (Collection of winning essays from FQXi's 2020 Essay Contest).
- 2021
Review of What is a Complex System? by James Ladyman and Karoline Wiesner. BJPS Review of Books.[Journal Version (Open Access)]
- 2018
Imprecise Bayesian Networks as Causal Models. Information. 9(9), 211. (Special Issue on Probabilistic Causal Modelling in Intelligent Systems)[Journal Version (Open Access)]
Dissertation
-
The Problem of Granularity for Scientific Explanation. PhD awarded November 30, 2019.
Other Writing
- Covered in Motherboard January 25 2021.
-
In a Complex Crisis, Scientists Cannot Avoid Making Value Judgments. Santa Fe Institute Transmission.Quoted in The Guardian April 5 2020.
-
Is Causation Scientific? Personal Blog.
Thanks to Chloe de Canson and Aron Vallinder for sharing this website template, and to Jon Kinney for suping it up.