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ERIC Number: ED478203
Record Type: Non-Journal
Publication Date: 2003-Apr
Pages: 57
Abstractor: N/A
Reference Count: N/A
Causal Inference and the Heckman Model.
Briggs, Derek. C.
In the social sciences, evaluating the effectiveness of a program or intervention often leads researchers to draw causal inferences from observational research designs. Bias in estimated causal effects becomes an obvious problem in such settings. This paper presents the Heckman Model as an approach sometimes applied to observational data for the purpose of estimating an unbiased causal effect. The paper shows how the Heckman model can be viewed as an extension of the linear regression model, and discusses in some detail the assumptions necessary before either approach can be used to make causal inferences. Linear regression and the Heckman Model can make different assumptions about the relationship between two equations in an underlying behavioral model: a response schedule and a selection function. Under linear regression the two equations are assumed to be independent; under the Heckman Model, the two equations are allowed to be correlated. The Heckman Model is particularly sensitive to the choice of variables included in the selection function. This is demonstrated empirically in the context of estimating the effect of commercial coaching programs on the Scholastic Assessment Test (SAT) performance of high school students. Coaching effects are estimated for both sections of the SAT using data from the National Education Longitudinal Study of 1988. Small changes in the selection function are shown to have a big impact on estimated coaching effects under the Heckman Model. (Contains 2 tables, 8 figures, and 42 references.) (Author/SLD)
Publication Type: Reports - Descriptive; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A