Results 41 to 50 of about 127 (80)

Bias formulas for violations of proximal identification assumptions in a linear structural equation model

Journal of Causal Inference
Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as proxies
Cobzaru Raluca, Welsch Roy, Finkelstein Stan, Ng Kenney, Shahn Zach   +4 more
doaj   +1 more source

Multivariate zero-inflated causal model for regional mobility restriction effects on consumer spending

Journal of Causal Inference
The COVID-19 pandemic presents challenges to both public health and the economy. Our objective is to examine how household expenditure, a significant component of private demand, reacts to changes in mobility. This investigation is crucial for developing
Hong Taekwon, Lu Wenbin, Yang Shu, Ghosh Pulak   +3 more
doaj   +1 more source

A clarification on the links between potential outcomes and do-interventions

Journal of Causal Inference
Most of the scientific literature on causal modeling considers the structural framework of Pearl and the potential-outcome framework of Rubin to be formally equivalent and therefore interchangeably uses do-interventions and the potential-outcome ...
De Lara Lucas
doaj   +1 more source

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

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

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

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