Results 1 to 10 of about 137 (85)

Testing for treatment effect twice using internal and external controls in clinical trials [PDF]

Journal of Causal Inference, 2023
Leveraging external controls – relevant individual patient data under control from external trials or real-world data – has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial patients given ...
Yi Yanyao, Zhang Ying, Du Yu, Ye Ting
doaj   +2 more sources

Role of placebo samples in observational studies [PDF]

Journal of Causal Inference
In an observational study, it is common to leverage known null effects to detect bias. One such strategy is to set aside a placebo sample – a subset of data immune from the hypothesized cause-and-effect relationship. Existence of an effect in the placebo
Ye Ting, He Qijia, Chen Shuxiao, Zhang Bo   +3 more
doaj   +2 more sources

Mediated probabilities of causation [PDF]

Journal of Causal Inference
We propose a set of causal estimands that we call “the mediated probabilities of causation.” These estimands quantify the probabilities that an observed negative outcome was induced via a mediating pathway versus a direct pathway in a stylized setting ...
Rubinstein Max, Cuellar Maria, Malinsky Daniel   +2 more
doaj   +2 more sources

Generalized coarsened confounding for causal effects: a large-sample framework [PDF]

Journal of Causal Inference
There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a class of generalized coarsened procedures for confounding.
Ghosh Debashis, Wang Lei
doaj   +2 more sources

Estimating average causal effects with incomplete exposure and confounders [PDF]

Journal of Causal Inference
Standard methods for estimating average causal effects require complete observations of the exposure and confounders. In observational studies, however, missing data are ubiquitous. Motivated by a study on the effect of prescription opioids on mortality,
Wen Lan, McGee Glen
doaj   +2 more sources

Estimating Causal Effects With Hidden Confounding Using Instrumental Variables and Environments. [PDF]

Electronic Journal of Statistics, 2023
Kim-Anh Do, Min Jin Ha, Hongxu Zhu
exaly   +2 more sources

Causal Mediation Analysis: From Simple to More Robust Strategies for Estimation of Marginal Natural (In)Direct Effects. [PDF]

Statistics Surveys, 2023
Elizabeth A. Stuart, Ina Koning, Trang Quynh Nguyen   +2 more
exaly   +2 more sources

Simple yet sharp sensitivity analysis for unmeasured confounding

Journal of Causal Inference, 2022
We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains
Peña Jose M.
doaj   +1 more source

Estimating marginal treatment effects under unobserved group heterogeneity

Journal of Causal Inference, 2022
This article studies the treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. By using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the ...
Hoshino Tadao, Yanagi Takahide
doaj   +1 more source

Conditional average treatment effect estimation with marginally constrained models

Journal of Causal Inference, 2023
Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population.
van Amsterdam Wouter A. C., Ranganath Rajesh   +1 more
doaj   +1 more source