About: Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Creator	Zhang, Yao Shen, Cong Lee, Hyun-Suk Lee, Jang-Won Van Der Schaar, Mihaela Zame, William
Source	ArXiv
abstract	Subgroup analysis of treatment effects plays an important role in applications from medicine to public policy to recommender systems. It allows physicians (for example) to identify groups of patients for whom a given drug or treatment is likely to be effective and groups of patients for which it is not. Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup. These approaches have several weaknesses: they rely on a particular algorithm for estimating ITE, they ignore (in)homogeneity within identified subgroups, and they do not produce good confidence estimates. This paper develops a new method for subgroup analysis, R2P, that addresses all these weaknesses. R2P uses an arbitrary, exogenously prescribed algorithm for estimating ITE and quantifies the uncertainty of the ITE estimation, using a construction that is more robust than other methods. Experiments using synthetic and semi-synthetic datasets (based on real data) demonstrate that R2P constructs partitions that are simultaneously more homogeneous within groups and more heterogeneous across groups than the partitions produced by other methods. Moreover, because R2P can employ any ITE estimator, it also produces much narrower confidence intervals with a prescribed coverage guarantee than other methods.
has issue date	2020-06-14(xsd:dateTime)
has license	arxiv
sha1sum (hex)	5601127bf9f37f0bbee6ee392496cbc96cc88273
resource representing a document's title	Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
resource representing a document's body	covid:5601127bf9f37f0bbee6ee392496cbc96cc88273#body_text
is schema:about of	named entity 'medicine' named entity 'confidence intervals' named entity 'algorithm' named entity 'TREATMENT EFFECT' named entity 'HAVE' named entity 'ESTIMATING' named entity 'PATIENTS' named entity 'THESE' named entity 'BEGIN' named entity 'subgroups' named entity 'estimating' named entity 'subgroup analysis' named entity 'constructs' named entity 'subgroup' named entity 'groups' named entity 'ITE' named entity 'arbitrary' named entity 'applications' named entity 'ignore' named entity 'demonstrate' named entity 'groups' named entity 'R2P' named entity 'estimating' named entity 'estimator' named entity 'treatment effects' named entity 'average treatment effect' named entity 'residuals' named entity 'causal effects' named entity 'alanine aminotransferase' named entity 'classification methods' named entity 'truncated distribution' named entity 'recursive partitioning' named entity 'clinical trial' named entity 'covariate' named entity 'treatment effects' named entity 'confidence interval' named entity 'subgroup analysis' named entity 'validation set' named entity 'treatment effects' named entity 'causal inference' named entity 'confidence intervals' named entity 'treatment effects' named entity 'data samples' named entity 'iid' named entity 'uniformly distributed' named entity 'treatment effects' named entity '95%' named entity 'residuals' named entity 'confidence intervals' named entity 'residuals' named entity 'prune the tree' named entity 'exchangeability' named entity 'Gaussian process' named entity 'recursive partitioning' named entity 'treatment effect' named entity 'treatment effects' named entity 'IHDP' named entity 'treatment effects' named entity 'Algorithm' named entity 'algorithm' named entity 'regressor' named entity 'Causal Inference' named entity 'data samples' named entity 'potential outcomes' named entity 'causal effects' named entity 'confidence intervals' named entity 'covariate' named entity 'boxplots' named entity '1, 2' named entity 'subgroup analysis' named entity 'lymphocyte count' named entity 'hyper-parameter' named entity 'confidence interval' named entity 'parametric model' named entity 'weighted sum' named entity 'confidence interval' named entity 'causal effects' named entity 'regression trees' named entity 'Random Forest' named entity 'hyper-parameter' named entity 'confidence interval' named entity 'recursive partitioning'

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