Results 21 to 30 of about 917,002 (254)

Causal Diagrams for Causal Inference: An Introduction [PDF]

, 2022
openIn ambito econometrico-statistico, per quantificare una relazione causa effetto è richiesto un dettagliato lavoro preliminare di conoscenza del contesto in cui si manifesta il fenomeno studiato, al fine di fornire argomenti utili ad attribuire una ...
BERTIN, NICOLA
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Polydesigns and Causal Inference

Biometrics, 2005
Summary In an increasingly common class of studies, the goal is to evaluate causal effects of treatments that are only partially controlled by the investigator. In such studies there are two conflicting features: (1) a model on the full cohort design and data can identify the causal effects of interest, but can be sensitive to extreme regions of that ...
Li, Fan, Frangakis, Constantine E.
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Ancestral Causal Inference [PDF]

CoRR, 2016
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly
Magliacane, S., Claassen, T., Mooij, J.
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Delay and knowledge mediation in human causal reasoning [PDF]

, 2002
Contemporary theories of causal induction have focussed largely on the question of how evidence in the form of covariations between causes and effects is used to compute measures of causal strength.
Buehner, Marc
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An Introduction to Causal Inference [PDF]

The International Journal of Biostatistics, 2010
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the ...
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Computational Causal Inference

CoRR, 2020
We introduce computational causal inference as an interdisciplinary field across causal inference, algorithms design and numerical computing. The field aims to develop software specializing in causal inference that can analyze massive datasets with a variety of causal effects, in a performant, general, and robust way.
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Causal dynamic inference [PDF]

Annals of Mathematics and Artificial Intelligence, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alexander Bochman, Dov M. Gabbay
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Inferring Causal Explanations [PDF]

, 1999
A popular approach to explanations amounts to backward chaining over logical implications encoding causal links. However, the resulting explanations are often unsatisfactory from a common-sense point of view. We define a framework allowing us to distinguish causal implication from mere logical implication.
Philippe Besnard, Marie-Odile Cordier
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A note on efficient minimum cost adjustment sets in causal graphical models [PDF]

, 2022
We study the selection of adjustment sets for estimating the interventional mean under an individualized treatment rule. We assume a non-parametric causal graphical model with, possibly, hidden variables and at least one adjustment set composed of ...
Rotnitzky, Andrea, Smucler, Ezequiel
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Asymptotic Causal Inference

CoRR, 2021
We investigate causal inference in the asymptotic regime as the number of variables approaches infinity using an information-theoretic framework. We define structural entropy of a causal model in terms of its description complexity measured by the logarithmic growth rate, measured in bits, of all directed acyclic graphs (DAGs), parameterized by the ...
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