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Micro-Econometrics for Policy, Program and Treatment Effects$
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Myoung-jae Lee

Print publication date: 2005

Print ISBN-13: 9780199267699

Published to Oxford Scholarship Online: February 2006

DOI: 10.1093/0199267693.001.0001

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Other approaches for hidden bias*

Other approaches for hidden bias*

(p.147) 6 Other approaches for hidden bias*
Micro-Econometrics for Policy, Program and Treatment Effects

Myoung-Jae Lee

Oxford University Press

This chapter continues the discussion of the preceding chapter on how to deal with hidden bias caused by unobserved differences between the treatment (T) and control (C) groups. The preceding chapter presented practical and basic approaches; this chapter shows other approaches for hidden bias. Sensitivity analysis examines how a finding obtained under no hidden bias assumption changes as hidden bias is allowed; sensitivity analysis is informative but stops short of giving a definite answer to hidden bias. The more conventional approach would be parametric ‘selection-correction’ or ‘control-function’ methods; these are relatively straightforward to implement, but may be too restrictive. Nonparametric ‘bounding’ approaches would perhaps be too unrestrictive, providing bounds on the treatment effect of interest which are typically too wide to be useful. An approach to avoid hidden bias by controlling for post-treatment covariates is available in some special circumstances.

Keywords:   hidden bias, sensitivity analysis, selection correction, bounding approach, controlling post-treatment covariates

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