Instrument-based estimation of full treatment effects with movers
The effect of the full treatment is a primary parameter of interest in policy evaluation, while often only the effect of a subset of treatment is estimated. We partially identify the local average treatment effect of receiving full treatment (LAFTE) using an instrumental variable that may induce individuals into only a subset of treatment (movers). We show that movers violate the standard exclusion restriction, necessary conditions on the presence of movers are testable, and partial identification holds under a double exclusion restriction. We identify movers in four empirical applications and estimate informative bounds on the LAFTE in three of them.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
Instrumental variable (IV) methods are used to estimate causal effects in settings with unobserved confounding, where we cannot directly experiment on the treatment variable. Instruments are variables which only affect t…
Instrumented Difference-in-Differences with Heterogeneous Treatment Effects
Many studies exploit variation in policy adoption timing across units as an instrument for treatment. This paper formalizes the underlying identification strategy as an instrumented difference-in-differences (DID-IV). In…
Valid Causal Inference with (Some) Invalid Instruments
Instrumental variable methods provide a powerful approach to estimating causal effects in the presence of unobserved confounding. But a key challenge when applying them is the reliance on untestable "exclusion" assumptio…
Causal InferencevalidEndogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments
Instrumental variables (IV) regression is a popular method for the estimation of the endogenous treatment effects. Conventional IV methods require all the instruments are relevant and valid. However, this is impractical …
validInterpreting Instrumental Variable Estimands with Unobserved Treatment Heterogeneity: The Effects of College Education
Many treatment variables used in empirical applications nest multiple unobserved versions of a treatment. I show that instrumental variable (IV) estimands for the effect of a composite treatment are IV-specific weighted …