Estimation of Heterogeneous Individual Treatment Effects With Endogenous Treatments

@article{Feng2019EstimationOH,
  title={Estimation of Heterogeneous Individual Treatment Effects With Endogenous Treatments},
  author={Qian Feng and Quang Hieu Vuong and Haiqing Xu},
  journal={Journal of the American Statistical Association},
  year={2019},
  volume={115},
  pages={231 - 240}
}
ABSTRACT This article estimates individual treatment effects (ITE) and its probability distribution in a triangular model with binary-valued endogenous treatments. Our estimation procedure takes two steps. First, we estimate the counterfactual outcome and hence, the ITE for every observational unit in the sample. Second, we estimate the ITE density function of the whole population. Our estimation method does not suffer from the ill-posed inverse problem associated with inverting a nonlinear… 
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References

SHOWING 1-10 OF 76 REFERENCES
Estimation of Heterogeneous Individual Treatment Effects with Endogenous Treatments
This paper estimates individual treatment effects in a triangular model with binary — valued endogenous treatments. Following the identification strategy established in Vuong and Xu (2015), we
Unconditional Quantile Treatment Effects Under Endogeneity
This article develops estimators for unconditional quantile treatment effects when the treatment selection is endogenous. We use an instrumental variable (IV) to solve for the endogeneity of the
Semiparametric instrumental variable estimation of treatment response models
Estimating Outcome Distributions for Compliers in Instrumental Variables Models
In Imbens and Ingrist (1994), Angrist, Imbens and Rubin (1996) and Imbens and Rubin (1997), assumptions have been outlined under which instrumental variables estimands can be given a causal
Treatment Effects With Censoring and Endogeneity
This article develops a nonparametric approach to identification and estimation of treatment effects on censored outcomes when treatment may be endogenous and have arbitrarily heterogenous effects.
Counterfactual mapping and individual treatment effects in nonseparable models with binary endogeneity
This paper establishes nonparametric identification of individual treatment effects in a nonseparable model with a binary endogenous regressor. The outcome variable may be continuous, discrete, or a
An IV Model of Quantile Treatment Effects
The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications.
Identification of Causal Effects Using Instrumental Variables
TLDR
It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.
Identification and Inference in Nonlinear Difference-in-Differences Models
TLDR
This paper develops an alternative approach to the widely used Difference-In-Difference (DID) method for evaluating the effects of policy changes by introducing a nonlinear model that permits changes over time in the effect of unobservables.
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