Results 1 to 10 of about 157 (97)

On the pitfalls of Gaussian likelihood scoring for causal discovery

Journal of Causal Inference, 2023
We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising
Schultheiss Christoph, Bühlmann Peter
doaj   +2 more sources

Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities. [PDF]

Entropy (Basel)
The causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables.
Chicharro D, Nguyen JK.
europepmc   +3 more sources

Evaluating Boolean relationships in Configurational Comparative Methods [PDF]

Journal of Causal Inference
Configurational Comparative Methods (CCMs) aim to learn causal structures from datasets by exploiting Boolean sufficiency and necessity relationships. One important challenge for these methods is that such Boolean relationships are often not satisfied in
De Souter Luna
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Estimating causal effects with the neural autoregressive density estimator

Journal of Causal Inference, 2021
The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional
Garrido Sergio, Borysov Stanislav, Rich Jeppe, Pereira Francisco   +3 more
doaj   +2 more sources

On the bias of adjusting for a non-differentially mismeasured discrete confounder

Journal of Causal Inference, 2021
Biological and epidemiological phenomena are often measured with error or imperfectly captured in data. When the true state of this imperfect measure is a confounder of an outcome exposure relationship of interest, it was previously widely believed that ...
Peña Jose M., Balgi Sourabh, Sjölander Arvid, Gabriel Erin E.   +3 more
doaj   +2 more sources

The Inflation Technique Completely Solves the Causal Compatibility Problem

Journal of Causal Inference, 2020
The causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical ...
Navascués Miguel, Wolfe Elie
doaj   +2 more sources

Comparison of open-source software for producing directed acyclic graphs [PDF]

Journal of Causal Inference
Many software packages have been developed to assist researchers in drawing directed acyclic graphs (DAGs), each with unique functionality and usability. We examine five of the most common software to generate DAGs: TikZ, DAGitty, ggdag, dagR, and igraph.
Pitts Amy J., Fowler Charlotte R.
doaj   +2 more sources

A Two-Stage Joint Modeling Method for Causal Mediation Analysis in the Presence of Treatment Noncompliance

Journal of Causal Inference, 2020
Estimating the effect of a randomized treatment and the effect that is transmitted through a mediator is often complicated by treatment noncompliance. In literature, an instrumental variable (IV)-based method has been developed to study causal mediation ...
Park Soojin, Kürüm Esra
doaj   +2 more sources

A comparison of causal inference methods for evaluating multiple treatment groups. [PDF]

J Nonparametr Stat
Chen S, Wu H, Zhao H.
europepmc   +2 more sources

Simple yet sharp sensitivity analysis for unmeasured confounding

Journal of Causal Inference, 2022
We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains
Peña Jose M.
doaj   +1 more source