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Leveraging Experimental Evaluations for Understanding Causal Mechanisms
Author(s) -
Peck Laura R.
Publication year - 2020
Publication title -
new directions for evaluation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.374
H-Index - 40
eISSN - 1534-875X
pISSN - 1097-6736
DOI - 10.1002/ev.20422
Subject(s) - leverage (statistics) , computer science , causal model , management science , program evaluation , mechanism (biology) , data science , artificial intelligence , epistemology , economics , medicine , philosophy , public administration , pathology , political science
Experimental evaluations—especially when grounded in theory‐based impact evaluation—can provide insights into the mechanisms that generate program impacts. This chapter details variants of experimental evaluation designs and also analytic strategies that leverage experimental evaluation data to learn about causal mechanisms. The design variants are poised to illuminate causal mechanisms related to program implementation and the contribution of selected components of multifaceted programs. The analysis strategies lend themselves to illuminating causal mechanisms related to participants’ responses to program components as well as to the contributions of selected program components themselves. The chapter offers an example from one, theory‐based impact evaluation, which embedded both design and analytic strategies to examine the extent to which specific program components and participant experiences might be identified as causal mechanisms. The particular value in using this theory‐based experimental strategy is that the results are rigorous and potentially highly relevant to policy and practice.

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