Results 81 to 90 of about 917,002 (254)
To Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and Butterfly-Bias
Journal of Causal Inference, 2015 “M-Bias,” as it is called in the epidemiologic literature, is the bias introduced by conditioning on a pretreatment covariate due to a particular “M-Structure” between two latent factors, an observed treatment, an outcome, and a “collider.” This ...
Ding Peng, Miratrix Luke W.
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Annals of Mathematics and Artificial IntelligenceAbstract Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers. In this article, we show how lifting can be applied to efficiently compute causal effects in relational domains.
Luttermann, Malte +3 more
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Origo: causal inference by compression [PDF]
Knowledge and Information Systems, 2017 Causal inference from observational data is one of the most fundamental problems in science. In general, the task is to tell whether it is more likely that X caused Y, or vice versa, given only data over their joint distribution. In this paper we propose a general inference framework based on Kolmogorov complexity, as well as a practical and computable
Kailash Budhathoki, Jilles Vreeken
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, 2021 Implement calculation of conditional independencies (#29, thanks @JosephCottam) Add causal fusion parser Implement Taheri Experimental Design workflow Add several mutation functions for probability distributions (#10, #47) Begin using black code ...
Jeremy Zucker +2 more
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Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
, 2015 Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference.
Matt J. Kusner +3 more
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The role of causal inference in health services research II: a framework for causal inference. [PDF]
, 2020 In a previous Hints and Kinks, we discussed the role of causal inference in tasks of health services research (HSR) using examples from health system interventions (Moser et al. 2020).
Zwahlen, Marcel +3 more
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Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
wiley +1 more source
Design and Analysis of Experiments in Networks: Reducing Bias from Interference
Journal of Causal Inference, 2016 Estimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units.
Eckles Dean, Karrer Brian, Ugander Johan
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FAST RESTRICTED CAUSAL INFERENCE [PDF]
Demonstratio Mathematica, 2000 Hidden variables are well known sources of disturbance when recovering belief networks from data based only on measurable variables. Hence models assuming existence of hidden variables are under development. This paper presents a new algorithm "accelerating" the known CI algorithm of Spirtes, Glymour and Scheines {Spirtes:93}.
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