Scientific Explanation

By Michael Strevens. 2005

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…and Schwind, 2004; Eberle, Kaplan, and Montague, 1961; Hankinson, 2001; Harman, 1965; Hempel and Oppenheim, 1948; Hospers, 1946; Hume, 1748; James, 2003, 2003; Kaplan, 1964; Kim, Khanna and Koyejo, 2016; Kment, 2014; Langley, 1987; Lulia Nastasia and Rakow, 2010; Machamer, Darden, and…”
Explaining Explanations in AI (2018, neutral): “In recent decades, work in the philosophy of science and epis-temology has paid increasing attention to theories of contrastive explanations and counterfactual causality (e.g. Kment (2006); Lewis (1973); Ruben (2004); Woodward and Zalta (2003)).”
Finding new facts; thinking new thoughts. (2012, unknown): “…an appeal to plausible causal, mechanistic relations; Bonawitz & Lombrozo, in press; Hempel & Oppenheim, 1948; Keil & Wilson, 2000; Keleman, 1999; Kitcher, 1989; Legare, Gelman, & Wellman, 2010; Lombrozo, 2006, 2007, 2012; Lombrozo & Carey, 2006; Salmon, 1984; Strevens, 2004; Woodward, 2009).”
Software Intensive Science (2014, background): “(Woodward 2009; Alexandrova 2008; Bokulich 2011) What is the epistemic status of the results of inquiry that is driven by models and how do these models relate to traditional concerns regarding error and uncertainty?”
Explanations in the wild. (2021, unknown): “This approach has focused on how well explanations subserve epistemic goals such as accurate prediction (Woodward, 2019) or how well they embody theoretic virtues such as parsimony or simplicity (Kuhn, 1977; Thagard, 1978).”
Research Design in Qualitative/Quantitative/Mixed Methods (2011, background): “Braithwaite (1955) writes that the purpose of science is theory and that, by extension, the purpose of research is to contrib ute theories or refinements of existing theories to science. Theory is a kind of abstraction, a simplification of reality that applies in similar circumstances and not just ”
Understanding Why: The cognitive science of explanation (2008, background): “In this symposium we consider the emerging experimental literature on explanation in relation to the philosophy of scientific explanation (Strevens, 2006; Trout, 2007), with the goal of cultivating a cognitive science of explanation.”
Advances in offender assessment and rehabilitation: Contributions of the risk–needs–responsivity approach (2004, background): “The value of theories lies in their ability to explain natural phenomena / including human behaviour and mental processes (Braithwaite, 1955).”
Do Not Cross Me: Optimizing the Use of Cross-Sectional Designs (2019, background): “…of causality that addresses elements 2 and 3 and is particularly relevant to scientific research is the manipulationist approach that suggests causal conclusions arise from observing the results of interventions that precede effects in time (Illari & Russo, 2014; Woodward, 2003, 2017).”
Inference to the Best Explanation (IBE) Versus Explaining for the Best Inference (EBI) (2015, background): “(For a review of those accounts, see Woodward 2014).”
Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models (2020, background): “• Deductive-Nomological Theories (Hempel and Oppenheim, 1948; Braithwaite, 1953; Popper, 1959; Hempel, 1967; Railton, 1978), one of the oldest logical theories of explanation, hold that explanations are deductive syllogisms.”
The role of explanation in discovery and generalization: evidence from category learning (2010, background): “…research in psychology has addressed this question directly (see Lombrozo, 2006), but a rich tradition from philosophy provides candidate theories that offer useful starting points for psychological theorizing (see Woodward, 2009, for a review of philosophical accounts of scientific explanation).”
Explanation and Abductive Inference (2012, background): “…(e.g., Anderson, Krull, & Weiner, 1996; Heider, 1958; Malle, 2004), education (e.g., Chi, 2000; Roscoe &Chi, 2008), and philosophy (e.g., Salmon, 1989; Woodward, 2010), which have respectively emphasized explanations for human behavior, explanations in pedagogy, and explanations in science.”
Explaining inferences in Bayesian networks (2008, background): “Like the existing approaches, we believe that probabilistic causal explanations [6] [7] can elucidate the causal history behind inferences when formulated as probabilistic rules [8]: if < conditions > then < conclusion > with probability < p > (1) where p is given by P (conclusion|conditions).”