Results 31 to 40 of about 157 (97)

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

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

Beyond conditional averages:Estimating the individual causal effect distribution [PDF]

In recent years, the field of causal inference from observational data has emerged rapidly. The literature has focused on (conditional) average causal effect estimation.
Post, Richard A.J., Van Den Heuvel, Edwin R.   +1 more
core   +2 more sources

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

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

The d-separation criterion in Categorical Probability

, 2022
The d-separation criterion detects the compatibility of a joint probability distribution with a directed acyclic graph through certain conditional independences.
Fritz, Tobias, Klingler, Andreas
core  

An optimal transport approach to estimating causal effects via nonlinear difference-in-differences

Journal of Causal Inference
We propose a nonlinear difference-in-differences (DiD) method to estimate multivariate counterfactual distributions in classical treatment and control study designs with observational data.
Torous William, Gunsilius Florian, Rigollet Philippe   +2 more
doaj   +1 more source

Mediated probabilities of causation

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   +1 more source

Almost exact Mendelian randomization

, 2023
Mendelian randomization (MR) is a natural experimental design based on the random transmission of genes from parents to offspring. However, this inferential basis is typically only implicit or used as an informal justification.
Smith, George Davey, Tudball, Matthew J, Zhao, Qingyuan   +2 more
core  

Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches

Journal of Causal Inference, 2023
We propose semiparametric and nonparametric methods to estimate conditional interventional indirect effects in the setting of two discrete mediators whose causal ordering is unknown. Average interventional indirect effects have been shown to decompose an
Rubinstein Max, Branson Zach, Kennedy Edward H.   +2 more
doaj   +1 more source