 Identification of the Direction of a Causal Effect by Instrumental VariablesJournal of Business & Economic Statistics, 2016, vol. 34, issue 2, 176-184 Abstract: This article provides a strategy to identify the existence and direction of a causal effect in a generalized nonparametric and nonseparable model identified by instrumental variables. The causal effect concerns how the outcome depends on the endogenous treatment variable. The outcome variable, treatment variable, other explanatory variables, and the instrumental variable can be essentially any combination of continuous, discrete, or “other” variables. In particular, it is not necessary to have any continuous variables, none of the variables need to have large support, and the instrument can be binary even if the corresponding endogenous treatment variable and/or outcome is continuous. The outcome can be mismeasured or interval-measured, and the endogenous treatment variable need not even be observed. The identification results are constructive, and can be empirically implemented using standard estimation results. Date: 2016 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:34:y:2016:i:2:p:176-184 Ordering information: This journal article can be ordered from DOI: 10.1080/07350015.2015.1021925 Access Statistics for this article Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals |
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