Results 1 to 10 of about 13 (13)

The variance of causal effect estimators for binary v-structures

Journal of Causal Inference, 2022
Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally ...
Kuipers Jack, Moffa Giusi
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Confidence in causal inference under structure uncertainty in linear causal models with equal variances

Journal of Causal Inference, 2023
Inferring the effect of interventions within complex systems is a fundamental problem of statistics. A widely studied approach uses structural causal models that postulate noisy functional relations among a set of interacting variables.
Strieder David, Drton Mathias
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Causality and independence in perfectly adapted dynamical systems

Journal of Causal Inference, 2023
Perfect adaptation in a dynamical system is the phenomenon that one or more variables have an initial transient response to a persistent change in an external stimulus but revert to their original value as the system converges to equilibrium.
Blom Tineke, Mooij Joris M.
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Potential outcome and decision theoretic foundations for statistical causality

Journal of Causal Inference, 2023
In a recent work published in this journal, Philip Dawid has described a graphical causal model based on decision diagrams. This article describes how single-world intervention graphs (SWIGs) relate to these diagrams.
Richardson Thomas S., Robins James M.
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On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder

Journal of Causal Inference, 2020
Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder. Suppose that the confounder is unobserved but a nondifferential proxy of it is observed.
Peña Jose M.
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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
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A phenomenological account for causality in terms of elementary actions

Journal of Causal Inference
Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables is obscure. Asking “what qualifies an action for being an intervention on
Janzing Dominik, Mejia Sergio Hernan Garrido   +1 more
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Spectral Bayesian network theory. [PDF]

Linear Algebra Appl, 2023
Duttweiler L, Thurston SW, Almudevar A.
europepmc   +1 more source

Spectral neighbor joining for reconstruction of latent tree Models. [PDF]

SIAM J Math Data Sci, 2021
Jaffe A, Amsel N, Aizenbud Y, Nadler B, Chang JT, Kluger Y.   +5 more
europepmc   +1 more source

Spectral top-down recovery of latent tree models. [PDF]

Inf inference, 2023
Aizenbud Y, Jaffe A, Wang M, Hu A, Amsel N, Nadler B, Chang JT, Kluger Y.   +7 more
europepmc   +1 more source