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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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Basics of treatment effect analysis

Basics of treatment effect analysis

Chapter:
(p.7) 2 Basics of treatment effect analysis
Source:
Micro-Econometrics for Policy, Program and Treatment Effects
Author(s):

Myoung-Jae Lee

Publisher:
Oxford University Press
DOI:10.1093/0199267693.003.0002

For a treatment and a response variable, the ‘causal effects’ of the former on the latter is of interest. This chapter introduces causality based on ‘potential-treated and untreated-responses’, and examines what type of treatment effects are identified. The basic way to identify treatment effect is to compare the average difference between the treatment and control (i.e., untreated) groups. For this to work, the treatment should determine which potential response is realized, but should otherwise be unrelated to the potential responses. Biases can result if this condition is not met due to some observed and unobserved variables affecting both the treatment and response. Avoiding such biases is one of the main tasks in causal analysis with observational data. The treatment effect framework has been used in statistics and medicine, has appeared in econometrics under the name ‘switching regression’, and is closely linked to ‘structural form equations’ in econometrics. Causality using potential responses gives a new look to the old workhorse ‘regression analysis’, enabling the interpretation of the regression parameters as causal parameters.

Keywords:   potential response, selection-on-observables, selection-on-unobservable, overt bias, hidden bias

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