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Letting Evidence Speak for Itself: Measuring Confidence in Mechanisms

Resource type
Authors/contributors
Title
Letting Evidence Speak for Itself: Measuring Confidence in Mechanisms
Abstract
This chapter argues that the credibility of causal mechanisms can be greatly increased by formulating them as statements that are both empirically falsifiable and empirically confirmable. Whether statements can be so depends on the potential availability of the relevant evidence (e.g., no evidence exists that can prove or disprove the existence of God, but good quality evidence is potentially available in many other cases). The Bayes formula can be used to measure the extent to which a given set of empirical observations supports or weakens the belief that a causal mechanism exists. With this approach, confidence in the existence of a mechanism is increased or decreased through a process that can be open, transparent, and shared with the public or groups of stakeholders, reducing cognitive biases, and improving internal validity and consensus around the existence of given mechanisms. The approach is showcased in the evaluation of a learning partnership whereby a knowledge product released by a research organization influenced policy at the municipal level.
Publication
New Directions for Evaluation
Volume
2020
Issue
167
Pages
27-43
Date
2020
Language
en
DOI
10.1002/ev.20420
ISSN
1534-875X
Short Title
Letting Evidence Speak for Itself
Accessed
2021-02-18T14:26:30Z
Library Catalog
Wiley Online Library
Citation
Befani, B., & D’Errico, S. (2020). Letting Evidence Speak for Itself: Measuring Confidence in Mechanisms. New Directions for Evaluation, 2020(167), 27–43. https://doi.org/10.1002/ev.20420