ArticleStat2024
Communication-Efficient Distributed Estimation of Causal Effects With High-Dimensional Data.
Xiaohan Wang, Jiayi Tong, Sida Peng, Yong Chen, Yang Ning
Abstract read
In one paragraphArticle in Stat, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
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0citing papers in PubMed
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What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
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3 · Its place in the literatureWho cites it
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4 · The recordCorrections and comments
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5 · Who and what moneyAuthors and funding
5 authors.
Xiaohan WangDepartment of Statistics and Data Science, Cornell University, Ithaca, New York, USA.
Jiayi TongDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Sida PengMicrosoft Research, Redmond, Washington, USA.
Yong ChenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Yang NingDepartment of Statistics and Data Science, Cornell University, Ithaca, New York, USA.
Funding
Coordinating Individually Measured Phenotypes to Advance Mental Health ResearchU24MH136069 · NIMH · YALE UNIVERSITY · PI Yong Chen, Cui Tao · 2024 to 2026
$9.8MPANDA-MSD: Predictive Analytics via Networked Distributed Algorithms for Multi-System DiseasesU01TR003709 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI BIAN, JIANG, CHEN, YONG · 2022 to 2025
$4.7MTRiPOD: Toward Reusable Phenotypes in Observational Data for AD/ADRD - managing definitions and correcting biasR01AG073435 · NIA · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, XU, HUA · 2021 to 2025
$3.9MDynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4MClinEX - Clinical Evidence Extraction, Representation, and AppraisalR01LM014344 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Yong Chen, Yifan Peng · 2023 to 2026
$2.7MAI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methodsRF1AG077820 · NIA · UNIVERSITY OF PENNSYLVANIA · PI BIAN, JIANG, CHEN, YONG · 2023 to 2023
$2.3MPheBC: bias correction methods for EHR derived phenotypeR01LM013519 · NLM · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, XU, HUA · 2021 to 2024
$1.6MAdvancing Drug Repositioning for Alzheimer’s Disease using Real-world DataR56AG069880 · NIA · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, CHEN, YONG · 2021 to 2022
$1.6MCICADA: clinical informatics and computational approaches for drug-repositioning of AD/ADRDR56AG074604 · NIA · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, TAO, CUI · 2021 to 2022
$1.5MA General Framework to Account for Outcome Reporting Bias in Systematic ReviewsR01LM012607 · NLM · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG · 2017 to 2020
$1.4MSurrogate Augmented Deep Predictive Learning for Retinopathy of PrematurityR21EY034179 · NEI · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, HE, LIFANG · 2023 to 2023
$482kMethods for Estimating Disease Burden of Seasonal InfluenzaR21AI167418 · NIAID · EMORY UNIVERSITY · PI CHANG, HOWARD H, CHEN, YONG · 2023 to 2024
$443kAHRQ HHS R03 HS022900NCATS NIH HHS U01 TR003709NEI NIH HHS R21 EY034179NIAID NIH HHS R01 AI130460NIAID NIH HHS R21 AI167418NIA NIH HHS R01 AG073435NIA NIH HHS R56 AG069880NIA NIH HHS R56 AG074604NIA NIH HHS RF1 AG077820NIMH NIH HHS U24 MH136069NLM NIH HHS R01 LM012607NLM NIH HHS R01 LM013519NLM NIH HHS R01 LM014344
6 · The paper itselfAbstract
We propose a communication-efficient algorithm to estimate the average treatment effect (ATE), when the data are distributed across multiple sites and the number of covariates is possibly much larger than the sample size in each site. Our main idea is to calibrate the estimates of the propensity score and outcome models using some proper surrogate loss functions to approximately attain the desired covariate balancing property. We show that under possible model misspecification, our distributed covariate balancing propensity score estimator (disthdCBPS) can approximate the global estimator, obtained by pooling together the data from multiple sites, at a fast rate. Thus, our estimator remains consistent and asymptotically normal. In addition, when both the propensity score and the outcome models are correctly specified, the proposed estimator attains the semi-parametric efficiency bound. We illustrate the empirical performance of the proposed method in both simulation and empirical studies.
Indexed as
average treatment effectcausal inferencedistributed learninghigh-dimensional data
Identifiers
PMID40642290
PMCPMC12245182
What OpenQuestion holds
Textmetadata
LicenceTDM
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