Results 31 to 40 of about 27,731,435 (252)
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 +4 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
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
Individual differences in causal learning and decision making [PDF]
, 2005 This is an accepted author manuscript of an article subsequently published by Elsevier. The final published version can be found here: http://dx.doi.org/10.1016/j.actpsy.2005.04.003In judgment and decision making tasks, people tend to neglect the overall
Osman, M, Shanks, David R
core +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 +5 more
semanticscholar +1 more source
Beliefs and actions in the trust game : Creating instrumental variables to estimate the causal effect [PDF]
, 2012 In many economic contexts, an elusive variable of interest is the agent’s belief about relevant events, e.g. about other agents’ behavior. A growing number of surveys and experiments ask participants to state beliefs explicitly but little is known about ...
Weizsacker, Georg +5 more
core +1 more source
Sensitivity analysis for causal effects with generalized linear models
, 2022 Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed
Gabriel, EE +5 more
core +1 more source
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
doaj +1 more source
Causal Discovery in Probabilistic Networks with an Identifiable Causal Effect [PDF]
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest.
Vowels, Matthew J. +5 more
core +1 more source
Counterfactual Causal-Effect Intervention for Interpretable Medical Visual Question Answering
IEEE Transactions on Medical ImagingMedical Visual Question Answering (VQA-Med) is a challenging task that involves answering clinical questions related to medical images. However, most current VQA-Med methods ignore the causal correlation between specific lesion or abnormality features ...
Lin-Qin Cai +3 more
semanticscholar +1 more source