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Sensitivity Analysis in Observational Research: Introducing the E-Value
Key Summary Points Motivation: Observational studies that attempt to assess causality between a treatment and an outcome may be subject to unmeasured confounding. Rationale: Sensitivity analysis canExpand
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General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference
ABSTRACT Frequentists’ inference often delivers point estimators associated with confidence intervals or sets for parameters of interest. Constructing the confidence intervals or sets requiresExpand
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A paradox from randomization-based causal inference
Under the potential outcomes framework, causal effects are defined as comparisons between potential outcomes under treatment and control. To infer causal effects from randomized experiments, NeymanExpand
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Identifiability and Estimation of Causal Effects by Principal Stratification With Outcomes Truncated by Death
In medical studies, there are many situations where the final outcomes are truncated by death, in which patients die before outcomes of interest are measured. In this article we considerExpand
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On the Conditional Distribution of the Multivariate t Distribution
ABSTRACT As alternatives to the normal distributions, t distributions are widely applied in robust analysis for data with outliers or heavy tails. The properties of the multivariate t distributionExpand
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Asymptotic theory of rerandomization in treatment–control experiments
Significance Rerandomization refers to experimental designs that enforce covariate balance. This paper studies the asymptotic properties of the difference-in-means estimator under rerandomization,Expand
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Sensitivity Analysis Without Assumptions
TLDR
We derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Expand
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Randomization inference for treatment effect variation
TLDR
We propose a model-free approach for testing for the presence of unexplained treatment effect variation that is not explained by observed covariates. Expand
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Principal stratification analysis using principal scores
Practitioners are interested in not only the average causal effect of the treatment on the outcome but also the underlying causal mechanism in the presence of an intermediate variable between theExpand
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Overlap in Observational Studies with High-Dimensional Covariates
TLDR
We explore the population implications of overlap in observational studies with high-dimensional covariates and formalize curse-of-dimensionality argument, suggesting that these assumptions are stronger than investigators likely realize. Expand
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