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Confounding Equivalence in Causal Inference [PDF]
, 2012 The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment.
Paz, Azaria, Pearl, Judea
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How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
, 2018 Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users
Anderson C. +7 more
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A confounding bridge approach for double negative control inference on causal effects
Statistical Theory and Related FieldsUnmeasured confounding is a key challenge for causal inference. In this paper, we establish a framework for unmeasured confounding adjustment with negative control variables.
Wang Miao +3 more
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Cancer Medicine, 2021 Background The relation between obesity, blood pressure (BP) and bladder cancer (BC) risk and mortality remains unclear, partially due to potential confounding by smoking, the strongest risk factor for BC, and not accounting for tumor stage and grade in ...
Stanley Teleka +7 more
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Sparse Probit Linear Mixed Model
, 2017 Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various ...
Cunningham, John P. +5 more
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The Entry of Randomized Assignment into the Social Sciences
Journal of Causal Inference, 2019 Although the concept of randomized assignment in order to control for extraneous confounding factors reaches back hundreds of years, the first empirical use appears to have been in an 1835 trial of homeopathic medicine.
Jamison Julian C.
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Information Theoretic Causal Effect Quantification
Entropy, 2019 Modelling causal relationships has become popular across various disciplines. Most common frameworks for causality are the Pearlian causal directed acyclic graphs (DAGs) and the Neyman-Rubin potential outcome framework.
Aleksander Wieczorek, Volker Roth
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Why We Should Teach Causal Inference: Examples in Linear Regression With Simulated Data
Journal of Statistics Education, 2020 Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process.
Karsten Lübke +3 more
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Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects [PDF]
, 2011 Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or ...
Neil Lawrence +2 more
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DFW: a novel weighting scheme for covariate balancing and treatment effect estimation
Frontiers in Applied Mathematics and StatisticsEstimating causal effects from observational data is challenging due to selection bias, which leads to imbalanced covariate distributions across treatment groups.
Ahmad Saeed Khan +2 more
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