Results 11 to 20 of about 125 (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

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

Averaging causal estimators in high dimensions

Journal of Causal Inference, 2020
There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large.
Antonelli Joseph, Cefalu Matthew
doaj   +1 more source

Estimating causal effects with the neural autoregressive density estimator

Journal of Causal Inference, 2021
The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional
Garrido Sergio, Borysov Stanislav, Rich Jeppe, Pereira Francisco   +3 more
doaj   +1 more source

From urn models to box models: Making Neyman's (1923) insights accessible

Journal of Causal Inference
Neyman’s 1923 paper introduced the potential outcomes framework and the foundations of randomization-based inference. We discuss the influence of Neyman’s paper on four introductory to intermediate-level textbooks by Berkeley faculty members (Scheffé ...
Lin Winston, Dudoit Sandrine, Nolan Deborah, Speed Terence P.   +3 more
doaj   +1 more source

On the bias of adjusting for a non-differentially mismeasured discrete confounder

Journal of Causal Inference, 2021
Biological and epidemiological phenomena are often measured with error or imperfectly captured in data. When the true state of this imperfect measure is a confounder of an outcome exposure relationship of interest, it was previously widely believed that ...
Peña Jose M., Balgi Sourabh, Sjölander Arvid, Gabriel Erin E.   +3 more
doaj   +1 more source

Conditional generative adversarial networks for individualized causal mediation analysis

Journal of Causal Inference
Most classical methods popularly used in causal mediation analysis can only estimate the average causal effects and are difficult to apply to precision medicine.
Huan Cheng, Sun Rongqian, Song Xinyuan
doaj   +1 more source

Bias attenuation results for dichotomization of a continuous confounder

Journal of Causal Inference, 2022
It is well-known that dichotomization can cause bias and loss of efficiency in estimation. One can easily construct examples where adjusting for a dichotomized confounder causes bias in causal estimation.
Gabriel Erin E., Peña Jose M., Sjölander Arvid   +2 more
doaj   +1 more source

Prospective and retrospective causal inferences based on the potential outcome framework

Journal of Causal Inference
In this article, we discuss both prospective and retrospective causal inferences, building on Neyman’s potential outcome framework. For prospective causal inference, we review criteria for confounders and surrogates to avoid the Yule–Simpson paradox and ...
Geng Zhi, Zhang Chao, Wang Xueli, Liu Chunchen, Wei Shaojie   +4 more
doaj   +1 more source