Non Causal Theories and Using Auxiliary Assumptions to Handle Situation-Specificity

The article offers analytical tools for designing multi-actor implementation processes. It does so by proposing a design approach centred on causal mechanisms. Such design strategy requires designers to focus primarily on causal theories explaining why implementers commit overtime to implementing policies. The central proposal is that design procedures should be reversed, i.e. start by reasoning on the causal mechanisms explaining implementers’ behaviour and then go looking for design features. Several advantages of this approach related to designing, reforming, or transferring successful practices are discussed throughout the article. Finally, the article provides six extended examples of such mechanisms in different policy fields: actor’s certification, blame avoidance, earning brownie points, repeated interactions, focusing events and attribution of opportunity or threat.

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Causal Theories of Mental Content

Philosophy Compass ◽

10.1111/j.1747-9991.2008.00130.x ◽

2008 ◽

Vol 3 (2) ◽

pp. 353-380 ◽

Cited By ~ 10

Author(s):

Robert D. Rupert

Keyword(s):

Mental Content ◽

Causal Theories

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Proof and Uncertainty in Causal Claims

Exchanges The Interdisciplinary Research Journal ◽

10.31273/eirj.v5i2.238 ◽

2018 ◽

Vol 5 (2) ◽

pp. 72-89

Author(s):

Martine Jayne Barons ◽

Rachel L Wilkerson

Keyword(s):

Graphical Models ◽

Mathematical Proof ◽

Statistical Modelling ◽

Causal Mechanisms ◽

Science Domain ◽

History Of ◽

New Methodologies ◽

Mathematical Sciences ◽

Modelling Assumptions ◽

Causal Theories

Causal questions drive scientific enquiry. From Hume to Granger, and Rubin to Pearl the history of science is full of examples of scientists testing new theories in an effort to uncover causal mechanisms. The difficulty of drawing causal conclusions from observational data has prompted developments in new methodologies, most notably in the area of graphical models. We explore the relationship between existing theories about causal mechanisms in a social science domain, new mathematical and statistical modelling methods, the role of mathematical proof and the importance of accounting for uncertainty. We show that, while the mathematical sciences rely on their modelling assumptions, dialogue with the social sciences calls for continual extension of these models. We show how changing model assumptions lead to innovative causal structures and more nuanced casual explanations. We review differing techniques for determining cause in different disciplines using causal theories from psychology, medicine, and economics.

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Possible Worlds Counterfactual Theories of Causation

Canadian Journal of Philosophy Supplementary Volume ◽

10.1080/00455091.1980.10715761 ◽

1980 ◽

Vol 6 ◽

pp. 119-138

Author(s):

Richard Adler

Keyword(s):

Alternative Treatment ◽

Possible Worlds ◽

Great Length ◽

Current Interest ◽

Possible Worlds Semantics ◽

Causal Relations ◽

Its Analysis ◽

Counterfactual Theory ◽

Causal Theories ◽

Counterfactual Approach

The numerous difficulties facing the traditional Humean regularity approach to the problem of causation have been discussed in the literature at great length. In view of the current interest in possible worlds semantics, it is not surprising that the only serious alternative treatment of causation presently available, the counterfactual approach, has been explored recently as a means of circumventing the apparently unresolvable difficulties facing regularity causal theories. It is the purpose of this paper to suggest that such a strategy holds little promise. Specifically, I will argue that, in addition to giving rise to problems directly analogous to those facing regularity accounts, the counterfactual approach fails in principle to reflect important properties of causal relations as we understand them intuitively. David Lewis's possible worlds account, the most comprehensive counterfactual theory to date, is further criticized for implicit problems with natural lawhood even more serious than those typically raised for regularity accounts, for additional inadequacies in its analysis of causal relations, and for its failure to satisfy basic empiricist epistemological standards.

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Scepticism and Causal Theories of Knowledge

Mind ◽

10.1093/mind/lxxxvi.344.555 ◽

1977 ◽

Vol LXXXVI (344) ◽

pp. 555-573

Author(s):

A. J. HOLLAND

Keyword(s):

Causal Theories

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Causal theories

The Skills of Argument ◽

10.1017/cbo9780511571350.002 ◽

1991 ◽

pp. 21-43

Keyword(s):

Causal Theories

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Testing Causal Theories with Learned Proxies

Annual Review of Political Science ◽

10.1146/annurev-polisci-051120-111443 ◽

2022 ◽

Vol 25 (1) ◽

Author(s):

Dean Knox ◽

Christopher Lucas ◽

Wendy K. Tam Cho

Keyword(s):

Political Science ◽

Best Practice ◽

Computational Models ◽

Annual Review ◽

Publication Date ◽

Social Scientists ◽

Theoretical Concepts ◽

Point Estimates ◽

Causal Diagrams ◽

Causal Theories

Social scientists commonly use computational models to estimate proxies of unobserved concepts, then incorporate these proxies into subsequent tests of their theories. The consequences of this practice, which occurs in over two-thirds of recent computational work in political science, are underappreciated. Imperfect proxies can reflect noise and contamination from other concepts, producing biased point estimates and standard errors. We demonstrate how analysts can use causal diagrams to articulate theoretical concepts and their relationships to estimated proxies, then apply straightforward rules to assess which conclusions are rigorously supportable. We formalize and extend common heuristics for “signing the bias”—a technique for reasoning about unobserved confounding—to scenarios with imperfect proxies. Using these tools, we demonstrate how, in often-encountered research settings, proxy-based analyses allow for valid tests for the existence and direction of theorized effects. We conclude with best-practice recommendations for the rapidly growing literature using learned proxies to test causal theories. Expected final online publication date for the Annual Review of Political Science, Volume 25 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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