ArticleACM CHIL 2021 : proceedings of the 2021 ACM Conference on Health, Inference, and Learning : April 8-9, 2021, Virtual Event. ACM Conference on Health, Inference, and Learning (2021 : Online)2021
Enabling Counterfactual Survival Analysis with Balanced Representations.
Article in ACM CHIL 2021 : proceedings of the 2021 ACM Conference on Health, Inference, and Learning : April 8-9, 2021, Virtual Event. ACM Conference on Health, Inference, and Learning (2021 : Online), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Review
- Heterogeneous treatment effect estimation for observational data using model-based forests.Statistical methods in medical research · 2024Article
- Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR.Proceedings of machine learning research · 2022Article
- Estimating the Individual Treatment Effect on Survival Time Based on Prior Knowledge and Counterfactual Prediction.Entropy (Basel, Switzerland) · 2022Article
- BITES: balanced individual treatment effect for survival data.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Balanced representation learning methods have been applied successfully to counterfactual inference from observational data. However, approaches that account for survival outcomes are relatively limited. Survival data are frequently encountered across diverse medical applications,
Indexed as
Identifiers
What OpenQuestion holds
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.