Conditional Independance in Sample Selection Models
In this note, I describe the conditional independence properties that make it possible to use the selection propensity score to control selection bias in a general sample selection model. The resulting characterization does not rely on a latent index selection mechanism and imposes no structure other than an assumption of independance between the regression error term and the regressors in random samples. This approach leads to a simple rule that can be used to determine if conditioning on the selection propensity score is sufficient to control selection bias.
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"Sample Selection Bias as a Specification Error,"
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- Ham, John C & LaLonde, Robert J, 1996. "The Effect of Sample Selection and Initial Conditions in Duration Models: Evidence from Experimental Data on Training," Econometrica, Econometric Society, vol. 64(1), pages 175-205, January.
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- Joshua D. Angrist, 1995. "Conditioning on the Probability of Selection to Control Selection Bias," NBER Technical Working Papers 0181, National Bureau of Economic Research, Inc.
- Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-318, May.
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