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ERIC Number: EJ1112901
Record Type: Journal
Publication Date: 2016
Pages: 11
Abstractor: As Provided
ISSN: ISSN-0046-1520
Quasi-Experimental Designs for Causal Inference
Kim, Yongnam; Steiner, Peter
Educational Psychologist, v51 n3-4 p395-405 2016
When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This article introduces for each design the basic rationale, discusses the assumptions required for identifying a causal effect, outlines methods for estimating the effect, and highlights potential validity threats and strategies for dealing with them. Causal estimands and identification results are formalized with the potential outcomes notations of the Rubin causal model.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A