# 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…

## 11 Citations

Consistent Estimation of Treatment Effects Under Endogenous Heteroskedasticity

- Economics, Mathematics
- 2018

The empirical literature on program evaluation limits its scope almost exclusively to models where treatment effects are homogenous for observationally identical individuals. This paper considers a…

Counterfactual mapping and individual treatment effects in nonseparable models with binary endogeneity

- Economics, Mathematics
- 2017

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…

Identification and Estimation of Triangular Models with a Binary Treatment

- Economics, MathematicsSSRN Electronic Journal
- 2019

I study the identification and estimation of a nonseparable triangular model with an endogenous binary treatment. Unlike other studies, I do not impose rank invariance or rank similarity on the…

Externally Valid Treatment Choice

- Economics
- 2022

We consider the problem of learning treatment (or policy) rules that are externally valid in the sense that they have welfare guarantees in target populations that are similar to, but possibly…

Essays in Econometrics

- Economics
- 2020

Essays in Econometrics Junlong Feng My dissertation explores two broad areas in econometrics and statistics. The first area is nonparametric identification and estimation with endogeneity using…

Identification and Estimation of Nonseparable Models with Multivalued Endogeneity and a Binary Instrument

- Economics, Mathematics
- 2019

In this paper, I show that a nonseparable model where the endogenous variable is multivalued can be point-identified even when the instrument (IV) is only binary. Though the order condition generally…

Panel Data Quantile Regression for Treatment Effect Models

- EconomicsJournal of Business & Economic Statistics
- 2022

Inference on Individual Treatment Effects in Nonseparable Triangular Models

- Mathematics
- 2021

In nonseparable triangular models with a binary endogenous treatment and a binary instrumental variable, Vuong and Xu (2017) show that the individual treatment effects (ITEs) are identifiable. Feng,…

Identification of multi-valued treatment effects with unobserved heterogeneity

- Mathematics, Economics
- 2020

This paper establishes sufficient conditions for identifying the treatment effects on continuous outcomes in endogenous and multi-valued discrete treatment settings with unobserved heterogeneity and establishes identification of the local treatment effects inMulti-valued treatment settings and derive the closed-form expressions of the identified treatment effects.

## References

SHOWING 1-10 OF 76 REFERENCES

Estimation of Heterogeneous Individual Treatment Effects with Endogenous Treatments

- Economics, Mathematics
- 2016

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

- Economics, Mathematics
- 2007

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

- Economics, Mathematics
- 2003

Estimating Outcome Distributions for Compliers in Instrumental Variables Models

- Economics
- 1997

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

- Economics, Mathematics
- 2015

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

- Economics, Mathematics
- 2017

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…

Instrumental quantile regression inference for structural and treatment effect models

- Economics, Mathematics
- 2006

An IV Model of Quantile Treatment Effects

- Economics
- 2002

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

- Economics
- 1993

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

- Economics, Mathematics
- 2006

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.