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Assimilative causal inference [PDF]
Nature CommunicationsCausal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems.
Marios Andreou, Nan Chen, Erik Bollt
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Engineering, 2020 Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop explainable artificial intelligence (XAI) algorithms ...
Kun Kuang +9 more
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International Journal of Public Health, 2023 Objectives: The aging of the South African population could have profound implications for the independence and overall quality of life of older adults as life expectancy increases. While there is evidence that lifetime socio-economic status shapes risks
Keletso Makofane +4 more
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CoRR, 2022 Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
Wu, Yulun +5 more
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On the dimensional indeterminacy of one-wave factor analysis under causal effects
Journal of Causal Inference, 2023 It is shown, with two sets of indicators that separately load on two distinct factors, independent of one another conditional on the past, that if it is the case that at least one of the factors causally affects the other, then, in many settings, the ...
VanderWeele Tyler J. +1 more
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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.
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Estimating marginal treatment effects under unobserved group heterogeneity
Journal of Causal Inference, 2022 This article studies the treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. By using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the ...
Hoshino Tadao, Yanagi Takahide
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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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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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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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