NATURE'S CAPACITIES AND THEIR MEASUREMENT

By D. Braddon-Mitchell. 1991

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Citations in the corpus

Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects (2020, background): “In other words, choosing a model is a problem that needs to be addressed before starting to carry out any causal analysis: “no causes in, no causes out” Cartwright et al. (1994). However, once specified the causal model dictates how to interpret the data, thus effectively resolving any apparent “par”
A theory of causal learning in children: causal maps and Bayes nets. (2004, background): “Causal knowledge plays a central role in theories both in science (Cartwright, 1989; Salmon, 1984) and in everyday life (Gopnik & Wellman, 1994; Gopnik & Glymour, 2002).”
From covariation to causation: A causal power theory. (1997, background): “These assumptions specify the focal sets reasoners prefer for computing contrast: those in which plausible alternative causes are controlled (cf. Cartwright , 1989; Salmon, 1980).”
Building Micro-Foundations for the Routines, Capabilities, and Performance Links (2007, unknown): “There are no macro-level entities on the social domain that somehow possess capacities or dispositions to act (Cartwright, 1989) that make them capable of directly producing macro-level outcomes and there are no processes of interaction between macro-entities that take place on this level.”
Unpredictability in economic analysis, econometric modeling and forecasting (2014, background): “To paraphrase Cartwright (1989), ‘Breaks out (as in (21)) need breaks in (as in (23))’, so it cannot be logically impossible to foresee some shifts—though it may be difficult in practice.”
Statistics and causal inference: A review (2003, background): “Cartwright (1989) expressed this principle 2Even the theory of stochastic processes, which provides probabilistic characterization of certain dynamic phenomena, assumes a fixed density function over time-indexed variables.”
Toward a Unified Theory of Causality (2008, unknown): “Also see Cartwright (1989); Eells (1991); Eells and Sober (1983); Good (1961, 1962); Lewis (2000); Menzies (2004); Pearl (2000); Skyrms (1980); Suppes (1970).”
Detecting blickets: how young children use information about novel causal powers in categorization and induction. (2000, background): “In fact, theoretical entities and categories in science are usually defined by their causal role (see e.g., Cartwright, 1989; Salmon, 1984).”
Causal inference (1991, background): “If the causal graph were in turn a function of the population P(V), then P(V||P’(V)) could still be calculated from P(V) alone.”
A Multilingual Perspective on Translanguaging (2017, background): “‘‘Frictionless planes, point masses, infinite velocities, isolated systems, omniscient agents, and markets in perfect equilibrium are but some well-known examples’’ of scientific idealization (Frigg & Hartmann, 2012; see also Cartwright, 1989; Nowak, 1972, 1992).”
A Survey of Learning Causality with Data: Problems and Methods (2018, background): ““No causes in, no causes out”, the famous quote fromCartwright [22] summarizes the procedure of learning causality with data.”
A dispositional theory of possibility (2008, background): “As far as we can tell, ‘capacity’ realists fall into this group as well; they include: Cartwright 1999 and Cartwright 1989; Dupré 1993.”
What are randomised controlled trials good for? (2009, background): “In this case the conditioning factors for deciding if C causes E need to include a dummy variable that takes value 1 just in case C has operated to produce the paired effect and the value 0 otherwise (Cartwright 1989, 2007).”