Results 51 to 60 of about 157 (97)

Heavy-tailed max-linear structural equation models in networks with hidden nodes

, 2023
Recursive max-linear vectors provide models for the causal dependence between large values of observed random variables as they are supported on directed acyclic graphs (DAGs).
Davison, Anthony C., Klüppelberg, Claudia, Krali, Mario   +2 more
core  

Beyond Manipulation: Administrative Sorting in Regression Discontinuity Designs

Journal of Causal Inference, 2020
This paper elaborates on administrative sorting, a threat to internal validity that has been overlooked in the regression discontinuity (RD) literature.
Crespo Cristian
doaj   +1 more source

Seemingly unrelated Bayesian additive regression trees for cost-effectiveness analyses in healthcare [PDF]

In recent years, theoretical results and simulation evidence have shown Bayesian additive regression trees to be a highly-effective method for nonparametric regression.
Bosmans, Judith, Esser, Jonas, Klausch, Thomas, Maia, Mateus, Murphy, Keefe, Parnell, Andrew C., van Dongen, Hanneke   +6 more
core   +2 more sources

Treatment effect estimation with observational network data using machine learning

Journal of Causal Inference
Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between them.
Emmenegger Corinne, Spohn Meta-Lina, Elmer Timon, Bühlmann Peter   +3 more
doaj   +1 more source

Causal structure learning in directed, possibly cyclic, graphical models

Journal of Causal Inference
We consider the problem of learning a directed graph G⋆{G}^{\star } from observational data. We assume that the distribution that gives rise to the samples is Markov and faithful to the graph G⋆{G}^{\star } and that there are no unobserved variables.
Semnani Pardis, Robeva Elina
doaj   +1 more source

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

Electron J Stat, 2023
Long JP, Zhu H, Do KA, Ha MJ.
europepmc   +1 more source

Energy balancing of covariate distributions

Journal of Causal Inference
Bias in causal comparisons has a correspondence with distributional imbalance of covariates between treatment groups. Weighting strategies such as inverse propensity score weighting attempt to mitigate bias by either modeling the treatment assignment ...
Huling Jared D., Mak Simon
doaj   +1 more source

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