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Dynamic treatment effects

James Heckman, John Humphries () and Gregory Veramendi ()

Journal of Econometrics, 2016, vol. 191, issue 2, 276-292

Abstract: This paper develops robust models for estimating and interpreting treatment effects arising from both ordered and unordered multi-stage decision problems. Identification is secured through instrumental variables and/or conditional independence (matching) assumptions. We decompose treatment effects into direct effects and continuation values associated with moving to the next stage of a decision problem. Using our framework, we decompose the IV estimator, showing that IV generally does not estimate economically interpretable or policy-relevant parameters in prototypical dynamic discrete choice models, unless policy variables are instruments. Continuation values are an empirically important component of estimated total treatment effects of education. We use our analysis to estimate the components of what LATE estimates in a dynamic discrete choice model.

Keywords: Choice theory; Dynamic treatment effects; Factor models; Marginal treatment effects; Regret; Conditional independence; Matching on mismeasured variables; Instrumental variables; Ordered choice models; Unordered choice models (search for similar items in EconPapers)
JEL-codes: C32 C38 D03 I12 I14 I21 (search for similar items in EconPapers)
Date: 2016
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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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