"Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify a local average treatment effect parameter using instrumental variables. More recently, suggested estimation of a variety of treatment effect parameters using a local version of their approach. We investigate the sensitivity of respective estimates to random departures from monotonicity. Approximations to respective bias terms are derived. In an empirical application the bias is calculated and bias corrected estimates are obtained. The accuracy of the approximation is investigated in a Monte Carlo study." [author's abstract
In heterogeneous treatment effect models with endogeneity, identification of the LATE typically rel...
This paper considers the problem of the identification of causal effects using instrumental variable...
Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to id...
Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify a ...
"Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify a...
In Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may...
Abstract This paper provides a review of methodological advancements in the evaluatio...
This paper provides a review of methodological advancements in the evaluation of heterogeneous treat...
Instrumental variables (IVs) are commonly used to estimate the effects of some treatments. A valid I...
Background Instrumental variable (IV) methods are often used to identify ‘local’ causal effects in ...
This chapter reviews instrumental variable models of quantile treatment effects. We focus on models ...
We investigate conditions sufficient for identification of average treatment effects using instrumen...
We consider the identification of the average treatment effect in models with continuous endogenous ...
Abstract: The instrumental variables (IV) method is a method for making causal in-ferences about the...
Instrumental variable (IV) analysis is used to address unmeasured confounding when comparing two non...
In heterogeneous treatment effect models with endogeneity, identification of the LATE typically rel...
This paper considers the problem of the identification of causal effects using instrumental variable...
Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to id...
Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify a ...
"Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify a...
In Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may...
Abstract This paper provides a review of methodological advancements in the evaluatio...
This paper provides a review of methodological advancements in the evaluation of heterogeneous treat...
Instrumental variables (IVs) are commonly used to estimate the effects of some treatments. A valid I...
Background Instrumental variable (IV) methods are often used to identify ‘local’ causal effects in ...
This chapter reviews instrumental variable models of quantile treatment effects. We focus on models ...
We investigate conditions sufficient for identification of average treatment effects using instrumen...
We consider the identification of the average treatment effect in models with continuous endogenous ...
Abstract: The instrumental variables (IV) method is a method for making causal in-ferences about the...
Instrumental variable (IV) analysis is used to address unmeasured confounding when comparing two non...
In heterogeneous treatment effect models with endogeneity, identification of the LATE typically rel...
This paper considers the problem of the identification of causal effects using instrumental variable...
Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to id...