Estimating Conditional Average Treatment Effects

@article{Abrevaya2014EstimatingCA,
  title={Estimating Conditional Average Treatment Effects},
  author={Jason Abrevaya and Yu‐Chin Hsu and Robert P. Lieli},
  journal={Journal of Business \& Economic Statistics},
  year={2014},
  volume={33},
  pages={485 - 505}
}
We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture the heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous covariates rather than the quantiles of the potential outcome distributions. We show that the CATE parameter is nonparametrically identified… 

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References

SHOWING 1-10 OF 37 REFERENCES

Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT

We propose inverse probability weighted estimators for the local average treatment effect (LATE) and the local average treatment effect for the treated (LATT) under instrumental variable assumptions

Efficient semiparametric estimation of multi-valued treatment effects under ignorability

Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score

It is shown that weighting with the inverse of a nonparametric estimate of the propensity Score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects, whether the pre-treatment variables have discrete or continuous distributions.

Semiparametric instrumental variable estimation of treatment response models

Nonparametric IV Estimation of Local Average Treatment Effects with Covariates

  • M. Frölich
  • Mathematics, Economics
    SSRN Electronic Journal
  • 2002

Nonparametric Tests of Conditional Treatment Effects

We develop a general class of nonparametric tests for treatment effects conditional on covariates. We consider a wide spectrum of null and alternative hypotheses regarding conditional treatment

Estimation and Inference for Generalized Full and Partial Means and Average Derivatives

We propose new semiparametric estimators for parameters that depend on the derivatives (up to any finite order) of unknown conditional expectations and densities. We consider two cases. In the first

On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects

The role of propensity score in the efficient estimation of the average treatment effects is examined. If the treatment is ignorable given some observed characteristics, it is shown that the

Dealing with limited overlap in estimation of average treatment effects

Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. This lack of

Semiparametric efficiency in nonlinear LATE models

In this paper we study semiparametric efficiency for the estimation of a finite- dimensional parameter defined by generalized moment conditions under the lo- cal instrumental variable assumptions.