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The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption (exogeneity of the...
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This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non-response bias, using two instrumental variables. Making use of a discrete instrument for the treatment and a continuous instrument for...
Persistent link: https://www.econbiz.de/10011348296
This paper proposes a new framework for analyzing the effects of sequences of treatments with duration outcomes. Applications include sequences of active labor market policies assigned at specific unemployment durations and sequences of medical treatments. We consider evaluation under...
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We propose methods for inference on the average effect of a treatment on a scalar outcome in the presence of very many controls. Our setting is a partially linear regression model containing the treatment/policy variable and a large number p of controls or series terms, with p that is possibly...
Persistent link: https://www.econbiz.de/10009419338
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This paper discusses the nonparametric identification of causal direct and indirect effects of a binary treatment based on instrumental variables. We identify the indirect effect, which operates through a mediator (i.e. intermediate variable) that is situated on the causal path between the...
Persistent link: https://www.econbiz.de/10010379279
This paper discusses the nonparametric identi.cation of causal direct and indirect effects of a binary treatment based on instrumental variables. We identify the indirect effect, which operates through a mediator (i.e. intermediate variable) that is situated on the causal path between the...
Persistent link: https://www.econbiz.de/10010382130