Results 1 to 10 of about 127 (80)

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

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

On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder

Journal of Causal Inference, 2020
Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder. Suppose that the confounder is unobserved but a nondifferential proxy of it is observed.
JOSÉ M Pena
exaly   +2 more sources

Estimating causal effects with hidden confounding using instrumental variables and environments [PDF]

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

A note on a sensitivity analysis for unmeasured confounding, and the related E-value

Journal of Causal Inference, 2020
Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding.
Arvid Sjölander
exaly   +2 more sources

Comparison of open-source software for producing directed acyclic graphs [PDF]

Journal of Causal Inference
Many software packages have been developed to assist researchers in drawing directed acyclic graphs (DAGs), each with unique functionality and usability. We examine five of the most common software to generate DAGs: TikZ, DAGitty, ggdag, dagR, and igraph.
Pitts Amy J., Fowler Charlotte R.
doaj   +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 Stuart, Ina Koning, Trang 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