Results 61 to 70 of about 157 (97)

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

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   +1 more source

Current philosophical perspectives on drug approval in the real world

Journal of Causal Inference
The evidence-based medicine approach to causal medical inference is the dominant account among medical methodologists. Competing approaches originating in the philosophy of medicine seek to challenge this account.
Landes Jürgen, Auker-Howlett Daniel J.
doaj   +1 more source

Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population. [PDF]

J Causal Inference, 2022
Lee D, Yang S, Wang X.
europepmc   +1 more source

A Geometric Perspective on Double Robustness by Semiparametric Theory and Information Geometry

Double robustness (DR) is a widely-used property of estimators that provides protection against model misspecification and slow convergence of nuisance functions.
Ying, Andrew
core  

Foundations of causal discovery on groups of variables

Journal of Causal Inference
Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models.
Wahl Jonas, Ninad Urmi, Runge Jakob
doaj   +1 more source

Dual Likelihood for Causal Inference under Structure Uncertainty

Knowledge of the underlying causal relations is essential for inferring the effect of interventions in complex systems. In a widely studied approach, structural causal models postulate noisy functional relations among interacting variables, where the ...
Drton, Mathias, Strieder, David
core  

Combining observational and experimental data for causal inference considering data privacy

Journal of Causal Inference
Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational datasets cannot be released due to data privacy considerations, so one researcher may not have access to both ...
Mann Charlotte Z., Sales Adam C., Gagnon-Bartsch Johann A.   +2 more
doaj   +1 more source

Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects. [PDF]

Stat Surv, 2023
Nguyen TQ, Ogburn EL, Schmid I, Sarker EB, Greifer N, Koning IM, Stuart EA.   +6 more
europepmc   +1 more source

Valid causal inference with unobserved confounding in high-dimensional settings

Journal of Causal Inference
Various methods have recently been proposed to estimate causal effects with confidence intervals that are uniformly valid over a set of data-generating processes when high-dimensional nuisance models are estimated by post-model-selection or machine ...
Moosavi Niloofar, Gorbach Tetiana, de Luna Xavier   +2 more
doaj   +1 more source

Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes. [PDF]

J Causal Inference, 2022
Nguyen TQ, Schmid I, Ogburn EL, Stuart EA.   +3 more
europepmc   +1 more source