ArticleBiometrics2018
A Bayesian nonparametric approach to causal inference on quantiles.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation.Gut and liver · 2024Review
- Confounding-adjustment methods for the causal difference in medians.BMC medical research methodology · 2023Article
- Adjusting for selection bias due to missing data in electronic health records-based research.Statistical methods in medical research · 2021Article
- A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches.Statistics in medicine · 2021Article
- On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value.The American statistician · 2020Article
- Discussion of PENCOMP.Journal of the American Statistical Association · 2019Article
- Bayesian nonparametric generative models for causal inference with missing at random covariates.Biometrics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.