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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 treatment effect model in which treatment effects may be heterogeneous, even among observationally identical individuals. Specifically, extending the classical instrumental variables (IV) model with an endogenous binary treatment and a binary instrument, we allow the heteroskedasticity of the error disturbance to also depend upon the treatment variable so that treatment has both mean and variance effects on the outcome. In this endogenous heteroskedasticity IV (EHIV) model with heterogeneous individual treatment effects, the standard IV estimator can be inconsistent and lead to incorrect inference. After showing identification of the mean and variance treatment effects in a nonparametric version of the EHIV model, we provide closed-form estimators for the linear EHIV for the mean and variance treatment effects and the individual treatment effects (ITE). Asymptotic properties of the estimators are provided. A Monte Carlo simulation investigates the performance of the proposed approach, and an empirical application regarding the effects of fertility on female labor supply is considered.
In observational studies, balancing covariates in different treatment groups is essential to estimate treatment effects. One of the most commonly used methods for such purposes is weighting. The performance of this class of methods usually depends on
We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be thick tailed, where treatment effects are small, where sample sizes are large and where assignment is completely rand
Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifia
Estimation of heterogeneous treatment effects is an essential component of precision medicine. Model and algorithm-based methods have been developed within the causal inference framework to achieve valid estimation and inference. Existing methods suc
In this paper, we study the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile