David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
Synthese 163 (3):419 - 432 (2008)
In their recent book, Is Inequality Bad for Our Health?, Daniels, Kennedy, and Kawachi claim that to “act justly in health policy, we must have knowledge about the causal pathways through which socioeconomic (and other) inequalities work to produce differential health outcomes.” One of the central problems with this approach is its dependency on “knowledge about the causal pathways.” A widely held belief is that the randomized clinical trial (RCT) is, and ought to be the “gold standard” of evaluating the causal efficacy of interventions. However, often the only data available are non-experimental, observational data. For such data, the necessary randomization is missing. Because the randomization is missing, it seems to follow that it is not possible to make epistemically warranted claims about the causal pathways. Although we are not sanguine about the difficulty in using observational data to make warranted causal claims, we are not as pessimistic as those who believe that the only warranted causal claims are claims based on data from (idealized) RCTs. We argue that careful, thoughtful study design, informed by expert knowledge, that incorporates propensity score matching methods in conjunction with instrumental variable analyses, provides the possibility of warranted causal claims using observational data.
|Keywords||Causal inference Confounding Social epidemiology Propensity scores Instrumental variables Methodology|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
William R. Shadish (2001). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.
Jaakko Hintikka (1975). The Intensions of Intentionality and Other New Models for Modalities. Dordrecht: D. Reidel.
Ariel Linden & John L. Adams (2006). Evaluating Disease Management Programme Effectiveness: An Introduction to Instrumental Variables. Journal of Evaluation in Clinical Practice 12 (2):148-154.
Peter Urbach (1985). Randomization and the Design of Experiments. Philosophy of Science 52 (2):256-273.
Citations of this work BETA
No citations found.
Similar books and articles
Frederick Eberhardt (2009). Introduction to the Epistemology of Causation. Philosophy Compass 4 (6):913-925.
Nancy Cartwright (1984). Causation in Physics: Causal Processes and Mathematical Derivations. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1984:391 - 404.
Jonathan Schaffer (2012). Causal Contextualisms. In Martijn Blaauw (ed.), Contrastivism in Philosophy: New Perspectives. Routledge
Joseph Berkovitz (2002). On Causal Inference in Determinism and Indeterminism. In Harald Atmanspacher & Robert C. Bishop (eds.), Between Chance and Choice: Interdisciplinary Perspectives on Determinism. Thorverton Uk: Imprint Academic 237--278.
Daniel Murray Hausman (2005). Causal Relata: Tokens, Types, or Variables? [REVIEW] Erkenntnis 63 (1):33 - 54.
Richard Scheines, Matt Easterday & David Danks (2007). Teaching the Normative Theory of Causal Reasoning. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press 119--38.
Kevin B. Korb & Erik Nyberg (2006). The Power of Intervention. Minds and Machines 16 (3):289-302.
Peter Spirtes (2005). Graphical Models, Causal Inference, and Econometric Models. Journal of Economic Methodology 12 (1):3-34.
Added to index2009-01-28
Total downloads15 ( #232,828 of 1,792,149 )
Recent downloads (6 months)2 ( #345,572 of 1,792,149 )
How can I increase my downloads?