Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies

DOI10.1080/01621459.2020.1870476zbMath1464.62209arXiv1802.06710OpenAlexW3118349612MaRDI QIDQ4999132

Dylan S. Small, Francesca Dominici, Kwonsang Lee

Publication date: 6 July 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1802.06710

zbMATH Keywords

recursive partitioningcausal effectcausal inferenceunmeasured confoundingparticulate mattersample split

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Causal inference from observational studies (62D20)

Related Items

Detecting heterogeneous treatment effects with instrumental variables and application to the Oregon Health Insurance Experiment, Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach, Designing experiments toward shrinkage estimation

Uses Software

• DOS

Cites Work

• Unnamed Item
• Unnamed Item
• Testing one hypothesis twice in observational studies
• Matching methods for causal inference: a review and a look forward
• Recursive partitioning and applications
• Computation of multivariate normal and \(t\) probabilities
• On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed
• Covariance adjustment in radomized experiments and observational studies
• Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion)
• Recursive partitioning for heterogeneous causal effects
• Amplification of Sensitivity Analysis in Matched Observational Studies
• Asymptotic Separability in Sensitivity Analysis
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Effect Modification and Design Sensitivity in Observational Studies
• Full Matching in an Observational Study of Coaching for the SAT
• Design of observational studies
• Observational studies.