Basics of treatment effect analysis

Myoung-Jae Lee

in Micro-Econometrics for Policy, Program and Treatment Effects

Published in print April 2005 | ISBN: 9780199267699
Published online February 2006 | e-ISBN: 9780191603044 | DOI:

Series: Advanced Texts in Econometrics

 						Basics of treatment effect analysis

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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

Chapter.  19559 words.  Illustrated.

Subjects: Econometrics and Mathematical Economics

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