A causal exposure response function with local adjustment for confounding: estimating health effects of exposure to low levels of ambient fine particulate matter

DOI10.1214/20-AOAS1330zbMath1446.62286arXiv1806.00928MaRDI QIDQ2194470

Georgia Papadogeorgou, Francesca Dominici

Publication date: 26 August 2020

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.00928

zbMATH Keywords

air pollutioncausal inferenceparticulate matterlocal confoundingcardiovascular hospitalizationsexposure response functionlow-exposure levels

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to environmental and related topics (62P12)

Related Items (2)

Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning ⋮ Generalized Propensity Score Approach to Causal Inference with Spatial Interference

Uses Software

• Stata
• SuperLearner
• doseresponse
• drf

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Understanding predictive information criteria for Bayesian models
• Estimating exposure response functions using ambient pollution concentrations
• Generalized additive models
• Optimal Bayesian-feasible dose escalation for cancer phase I trials
• Inference from iterative simulation using multiple sequences
• High-dimensional confounding adjustment using continuous Spike and Slab priors
• Bayesian curve-fitting with free-knot splines
• Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models
• Covariate selection for the nonparametric estimation of an average treatment effect
• Confounder selection via penalized credible regions
• Automatic Bayesian Curve Fitting
• Air Pollution and Mortality
• Bayesian Effect Estimation Accounting for Adjustment Uncertainty
• On model selection and model misspecification in causal inference
• Model averaged double robust estimation
• Non-parametric Methods for Doubly Robust Estimation of Continuous Treatment Effects
• Super Learner
• Causal Inference With General Treatment Regimes

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