Results 31 to 40 of about 16,076,570 (308)
Average Causal Effect Estimation Via Instrumental Variables: the No Simultaneous Heterogeneity Assumption [PDF]
Epidemiology, 2020 Background: Instrumental variables (IVs) can be used to provide evidence as to whether a treatment X has a causal effect on an outcome Y. Even if the instrument Z satisfies the three core IV assumptions of relevance, independence, and exclusion ...
F. Hartwig +3 more
semanticscholar +1 more source
Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction
Journal of Causal Inference, 2019 Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional
Luo Wei, Wu Wenbo, Zhu Yeying
doaj +1 more source
Local Search for Efficient Causal Effect Estimation [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2020 Causal effect estimation from observational data is a challenging problem, especially with high dimensional data and in the presence of unobserved variables.
Debo Cheng +4 more
semanticscholar +1 more source
Local Causal Discovery for Estimating Causal Effects
, 2023 Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class.
Gupta, Shantanu +2 more
openaire +2 more sources
Nature Genetics, 2018 Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR.
M. Verbanck +3 more
semanticscholar +1 more source
Frontiers in Endocrinology, 2023 BackgroundBreast and thyroid cancer are increasingly prevalent, but it remains unclear whether the observed associations are due to heightened medical surveillance or intrinsic etiological factors.
Hong Tan +5 more
doaj +1 more source
Causal Effect Identification in Uncertain Causal Networks
, 2022 27 pages, 9 figures, NeurIPS 2023 conference, causal identification, causal discovery, probabilistic ...
Akbari, Sina +5 more
openaire +2 more sources
Clustering of causal graphs to explore drivers of river discharge
Environmental Data Science, 2023 This work aims to classify catchments through the lens of causal inference and cluster analysis. In particular, it uses causal effects (CEs) of meteorological variables on river discharge while only relying on easily obtainable observational data.
Wiebke Günther +3 more
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Causality Is an Effect, II [PDF]
Entropy, 2021 Causality follows the thermodynamic arrow of time, where the latter is defined by the direction of entropy increase. After a brief review of an earlier version of this article, rooted in classical mechanics, we give a quantum generalization of the results. The quantum proofs are limited to a gas of Gaussian wave packets.
openaire +3 more sources
Mendelian Randomization analysis of the causal effect of adiposity on hospital costs
Journal of Health Economics, 2020 Estimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Most estimates of this association are affected by
Padraig Dixon +4 more
semanticscholar +1 more source