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Sharp bounds for variance of treatment effect estimators in the finite population in the presence of covariates
In the completely randomized experiment, the variances of treatment effe...
11/19/2020 ∙ by Ruoyu Wang, et al. ∙
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Semiparametric proximal causal inference
Skepticism about the assumption of no unmeasured confounding, also known...
11/17/2020 ∙ by Yifan Cui, et al. ∙
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Improving efficiency of inference in clinical trials with external control data
Suppose we are interested in the effect of a treatment in a clinical tri...
11/14/2020 ∙ by Xinyu Li, et al. ∙
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Identifying effects of multiple treatments in the presence of unmeasured confounding
Identification of treatment effects in the presence of unmeasured confou...
11/09/2020 ∙ by Wang Miao, et al. ∙
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An Introduction to Proximal Causal Learning
A standard assumption for causal inference from observational data is th...
09/23/2020 ∙ by Eric J. Tchetgen Tchetgen, et al. ∙
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A Selective Review of Negative Control Methods in Epidemiology
Purpose of Review: Negative controls are a powerful tool to detect and a...
09/11/2020 ∙ by Xu Shi, et al. ∙
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Routing-Led Placement of VNFs in Arbitrary Networks
The ever increasing demand for computing resources has led to the creati...
01/30/2020 ∙ by Joseph Billingsley, et al. ∙
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A Confounding Bridge Approach for Double Negative Control Inference on Causal Effects (Supplement and Sample Codes are included)
Unmeasured confounding is a key challenge for causal inference. Negative...
08/15/2018 ∙ by Wang Miao, et al. ∙
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A Confounding Bridge Approach for Double Negative Control Inference on Causal Effects
Unmeasured confounding is a key challenge for causal inference. Negative...
08/15/2018 ∙ by Wang Miao, et al. ∙
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Multiply Robust Causal Inference With Double Negative Control Adjustment for Unmeasured Confounding
Unmeasured confounding is a threat to causal inference in observational ...
08/14/2018 ∙ by Xu Shi, et al. ∙
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