Bounding treatment effects by pooling limited information across observations

@inproceedings{Lee2021BoundingTE,
  title={Bounding treatment effects by pooling limited information across observations},
  author={Sokbae (Simon) Lee and Martin P. Weidner},
  year={2021}
}
We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited “pooling” of information across observations. Namely, the bounds are constructed as sample averages over… 

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References

SHOWING 1-10 OF 35 REFERENCES

Robust Confidence Intervals for Average Treatment Effects Under Limited Overlap

  • C. Rothe
  • Mathematics, Economics
    SSRN Electronic Journal
  • 2015
Estimators of average treatment effects under unconfounded treatment assignment are known to become rather imprecise if there is limited overlap in the covariate distributions between the treatment

Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness

We consider estimation and inference on average treatment effects under unconfoundedness conditional on the realizations of the treatment variable and covariates. Given nonparametric smoothness

Inference on Finite Population Treatment Effects Under Limited Overlap

This paper studies inference on finite population average and local average treatment effects under limited overlap, meaning some strata have a small proportion of treated or untreated units. We

Dealing with limited overlap in estimation of average treatment effects

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Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores

&NA; Causal inference with observational studies often relies on the assumptions of unconfoundedness and overlap of covariate distributions in different treatment groups. The overlap assumption is

Treatment Effect Bounds under Monotonicity Assumptions: An Application to Swan-Ganz Catheterization

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Large Sample Properties of Matching Estimators for Average Treatment Effects

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Irregular Identification, Support Conditions, and Inverse Weight Estimation

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Discretizing Unobserved Heterogeneity

We study discrete panel data methods where unobserved heterogeneity is revealed in a first step, in environments where population heterogeneity is not discrete. We focus on two‐step grouped