Results 31 to 40 of about 9,946,919 (251)
Vaccines, 2022 Though COVID-19 vaccines have shown high efficacy, real-world effectiveness at the population level remains unclear. Based on the longitudinal data on vaccination coverage and daily infection cases from fifty states in the United States from March to May
Rui Wang +3 more
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A Critical View of the Structural Causal Model
CoRR, 2020 In the univariate case, we show that by comparing the individual complexities of univariate cause and effect, one can identify the cause and the effect, without considering their interaction at all. In our framework, complexities are captured by the reconstruction error of an autoencoder that operates on the quantiles of the distribution. Comparing the
Tomer Galanti, Ofir Nabati, Lior Wolf
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Causality and tractable probabilistic models
Frontiers in Computer ScienceCausal assertions stem from an asymmetric relation between some variable's causes and effects, i.e., they imply the existence of a function decomposition of a model where the effects are a function of the causes without implying that the causes are ...
David Cruz, Jorge Batista, Jorge Batista
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Structural Causal Bottleneck Models
CoRRWe introduce structural causal bottleneck models (SCBMs), a novel class of structural causal models. At the core of SCBMs lies the assumption that causal effects between high-dimensional variables only depend on low-dimensional summary statistics, or bottlenecks, of the causes.
Simon Bing, Jonas Wahl, Jakob Runge
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A clarification on the links between potential outcomes and do-interventions
Journal of Causal InferenceMost of the scientific literature on causal modeling considers the structural framework of Pearl and the potential-outcome framework of Rubin to be formally equivalent and therefore interchangeably uses do-interventions and the potential-outcome ...
De Lara Lucas
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Standardizing Structural Causal Models
CoRRSynthetic datasets generated by structural causal models (SCMs) are commonly used for benchmarking causal structure learning algorithms. However, the variances and pairwise correlations in SCM data tend to increase along the causal ordering. Several popular algorithms exploit these artifacts, possibly leading to conclusions that do not generalize to ...
Weronika Ormaniec +4 more
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Reducing Causality to Functions with Structural Models
CoRR, 2023 47 pages, submitted to The British Journal for the Philosophy of ...
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Survivorship care plans (SCPs) summarize cancer treatment and guide risk‐based follow‐up for cancer survivors, yet remain difficult to create, share, and use. Stakeholder perspectives are needed to inform usable approaches.
Molly S. Talman +4 more
wiley +1 more source
A Linear “Microscope” for Interventions and Counterfactuals
Journal of Causal Inference, 2017 This note illustrates, using simple examples, how causal questions of non-trivial character can be represented, analyzed and solved using linear analysis and path diagrams.
Pearl Judea
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A practical method to control spatiotemporal confounding in environmental impact studies
MethodsX, 2018 Separating natural spatiotemporal variation from the impact of human activities has long been a challenge in environmental impact studies. To overcome this problem, a causal modelling method based on spatiotemporal data, integrated with existing ...
Rezvan Hatami
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