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Multi-Source Causal Inference Using Control Variates [article]

Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, Michael I. Jordan
2021 arXiv   pre-print
The key idea is to construct control variates using the datasets in which the ATE is not identifiable. We show theoretically that this reduces the variance of the ATE estimate.  ...  We propose a general algorithm to estimate causal effects from multiple data sources, where the ATE may be identifiable only in some datasets but not others.  ...  In such cases causal inference is possible only when the data source satisfies certain delicate assumptions.  ... 
arXiv:2103.16689v2 fatcat:5ixxaomfrnehpcuzuwxx5k24ay

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

Su Chu, Mengna Huang, Rachel Kelly, Elisa Benedetti, Jalal Siddiqui, Oana Zeleznik, Alexandre Pereira, David Herrington, Craig Wheelock, Jan Krumsiek, Michael McGeachie, Steven Moore (+3 others)
2019 Metabolites  
In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic  ...  It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies.  ...  BNs can be used to build predictive models of case/control status [107] .  ... 
doi:10.3390/metabo9060117 pmid:31216675 pmcid:PMC6630728 fatcat:cqqo3h4qizhopksyhuxn6alwnm

Page 329 of American Sociological Review Vol. 24, Issue 3 [page]

1959 American Sociological Review  
Sorting all sources of variation into four classes seems to me a useful simplifica- tion.  ...  The control may be ex- ercised in either or both the selection and the estimation procedures. - ey] 4See the excellent and readable article, Herman Wold, “Causal Inference from Observational Data,” Journal  ... 

Ecological effects in multi-level studies

T. A Blakely
2000 Journal of Epidemiology and Community Health  
Identification of ecological eVects requires the minimisation of these sources of error, and a study design that captures suYcient variation in the ecological exposure of interest.  ...  Sources of error and weaknesses in study design that may aVect estimates of ecological eVects include: a lack of variation in the ecological exposure (and health outcome) in the available data; not allowing  ...  ENSURING VARIATION OF THE ECOLOGICAL EXPOSURE Table 2 Types 2 of fallacy in multi-level research (taken from Diez-Roux 30 ) Unit of analysis Level of inference Type of fallacy Group Individual  ... 
doi:10.1136/jech.54.5.367 pmid:10814658 pmcid:PMC1731678 fatcat:vanzushxevfxxlexmyk4o4fy74

Towards a Molecular Systems Model of Coronary Artery Disease

Gad Abraham, Oneil G. Bhalala, Paul I. W. de Bakker, Samuli Ripatti, Michael Inouye
2014 Current Cardiology Reports  
Traditionally, studies have analyzed only 1 disease factor at a time, providing useful but limited understanding of the underlying etiology.  ...  to be reactive to lipid levels through causal inference methods [59] .  ...  The advent of multi-omic studies, which concurrently analyze genetic variation, transcriptomics, metabolomics, and others sources of information over hundreds or thousands of individuals, has begun providing  ... 
doi:10.1007/s11886-014-0488-1 pmid:24743898 pmcid:PMC4050311 fatcat:qisj2yhzcjernlaumuk5m6kkja

Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence

Ellicott C. Matthay, Erin Hagan, Laura M. Gottlieb, May Lynn Tan, David Vlahov, Nancy Adler, M. Maria Glymour
2019 SSM: Population Health  
This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration.  ...  In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument  ...  Such multi-faceted causal links between an intervention and health threaten construct validity.  ... 
doi:10.1016/j.ssmph.2019.100526 pmid:31890846 pmcid:PMC6926350 fatcat:3i6zxq5ujzhfhiwt5kbaqpbpd4

Passive diagnosis for wireless sensor networks

Kebin Liu, Mo Li, Yunhao Liu, Minglu Li, Zhongwen Guo, Feng Hong
2008 Proceedings of the 6th ACM conference on Embedded network sensor systems - SenSys '08  
We propose PAD, a probabilistic diagnosis approach for inferring the root causes of abnormal phenomena.  ...  PAD employs a packet marking algorithm for efficiently constructing and dynamically maintaining the inference model.  ...  The recently proposed Sherlock is the only work that adopts a multi-state and multi-level inference graph for the network diagnosis [5] .  ... 
doi:10.1145/1460412.1460424 dblp:conf/sensys/LiuLLLGH08 fatcat:jbatxpbjcjg4bghm3mwhuizjiu

Passive diagnosis for wireless sensor networks

Kebin Liu, Mo Li, Xiaohui Yang, Mingxing Jiang
2008 Proceedings of the 6th ACM conference on Embedded network sensor systems - SenSys '08  
We propose PAD, a probabilistic diagnosis approach for inferring the root causes of abnormal phenomena.  ...  PAD employs a packet marking algorithm for efficiently constructing and dynamically maintaining the inference model.  ...  The recently proposed Sherlock is the only work that adopts a multi-state and multi-level inference graph for the network diagnosis [5] .  ... 
doi:10.1145/1460412.1460457 dblp:conf/sensys/LiuLYJ08 fatcat:xev7rx5xlrc5vpv2zkt6ekfyfy

Coal-Miner: A Coalescent-Based Method For GWA Studies Of Quantitative Traits With Complex Evolutionary Origins [article]

Hussein A. Hejase, Natalie Vande Pol, Gregory M. Bonito, Patrick P. Edger, Kevin J. Liu
2017 bioRxiv   pre-print
Using synthetic and empirical datasets, we compare the statistical power and type I error control of Coal-Miner against state-of-the-art AM methods.  ...  The initial stages of Coal-Miner seek to detect candidate loci, or loci which contain putatively causal markers.  ...  in multi-locus sequence evolution, particularly regarding the source(s) of local genealogical discordance.  ... 
doi:10.1101/132951 fatcat:xh436vefx5dkhgic2kgdbbroxm

Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies [article]

Lu Cheng, Ruocheng Guo, Huan Liu
2022 arXiv   pre-print
The defining challenge of causal inference with observational data is the presence of "confounder", which might not be observed or measured, e.g., consumers' preference to food type, rendering the estimated  ...  To address this challenge, we have recourse to the multi-modal proxies such as the consumer profile information and interactions between consumers and businesses.  ...  This finding suggests that directly using representation of multi-modal proxies without control of 'bad' variables can induce undesired biases and the influence is large.  ... 
arXiv:2112.10274v2 fatcat:2yvfnkpgfzhgpkhd2sal77rwbu

Optimization of Load Allocation Strategy of a Multi-source Energy System by Means of Dynamic Programming

Agostino Gambarotta, Mirko Morini, Nicola Pompini, Pier Ruggero Spina
2015 Energy Procedia  
chiller) which use renewable, partially renewable and fossil energy sources.  ...  This result is therefore very helpful both in comparing different solutions and in subsequently define a proper causal control strategy.  ...  Regarding energy system applications, the DP algorithm is widely used in dealings with multi-source energy plants. Marano et al.  ... 
doi:10.1016/j.egypro.2015.12.056 fatcat:2lqwn5enxfcanbrkbgehpse4nm

Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication [article]

Lu Cheng, Ruocheng Guo, Kasim Selcuk Candan, Huan Liu
2022 arXiv   pre-print
We draw on recent advances in machine learning and causal inference to together estimate the hidden confounders and causal effects.  ...  We present empirical evaluations using real-world examples to discuss the importance and implications of differentiating the multi-aspect effects in strategizing business operations.  ...  To address the defining challenge in causal inference -confounding -we employ a multiple-causal-inference framework with hidden confounders and leverage the advanced techniques in causal learning to control  ... 
arXiv:2110.01746v2 fatcat:5vskxgkfxza3zhortrkdiv6ycy

Causal modeling in a multi-omic setting: insights from GAW20

Jonathan Auerbach, Richard Howey, Lai Jiang, Anne Justice, Liming Li, Karim Oualkacha, Sergi Sayols-Baixeras, Stella W. Aslibekyan
2018 BMC Genetics  
and epigenomic variation on lipid phenotypes, as well as to validate prior findings from observational studies.  ...  The GAW20 Causal Modeling Working Group has applied complementary approaches (eg, Mendelian randomization, structural equations modeling, Bayesian networks) to discover novel causal effects of genomic  ...  Conclusions The experience of the GAW20 Causal Modeling Working Group illustrated several challenges and promises of causal inference in the multi-omic data environment.  ... 
doi:10.1186/s12863-018-0645-4 pmid:30255779 pmcid:PMC6157026 fatcat:ppow5xxwmvft3bfatkotkcrj74

Democratic institutions and the energy intensity of well-being: a cross-national study

Adam Mayer
2017 Energy, Sustainability and Society  
Thus, a key sustainability challenge is to efficiently use energy consumption to promote human well-being.  ...  Methods: We use international data to understand how democratic institutions-understood as a combination of elected legislature, elected executives, and democratic competition-impact the energy intensity  ...  In doing so, we used causal inference methods to isolate the effect of democracy on the energy intensity of well-being. We adopted a multi-faceted understanding of democracy informed by [26] .  ... 
doi:10.1186/s13705-017-0139-7 fatcat:yymxy2miybfsjbsgvkbvicxpjm


Yu Xie
2011 Information, Knowledge, Systems Management  
Because of population heterogeneity, causal inference with observational data in social science may suffer from two possible sources of bias: (1) bias in unobserved pretreatment factors affecting the outcome  ...  Even when we control for observed covariates, these two biases may occur if the classic ignorability assumption is untrue.  ...  In conclusion, I wish to warn researchers hoping to draw causal inferences in social science, particularly when using observational data, of several potential sources of bias caused by population heterogeneity  ... 
doi:10.3233/iks-2012-0197 pmid:23970824 pmcid:PMC3747843 fatcat:fahsw663premthzbrgfivqbl4q
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