Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning

• URL: http://arxiv.org/abs/2306.01086v3
• Date: Wed, 24 Apr 2024 15:19:52 GMT
• Title: Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
• Authors: Cathy Shyr, Boyu Ren, Prasad Patil, Giovanni Parmigiani,
• Abstract summary: Estimating heterogeneous treatment effects (HTEs) is crucial for precision medicine. Existing approaches often assume identical HTEs across studies. We propose a framework for multi-study HTE estimation.
• Score: 1.1045045527359925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating heterogeneous treatment effects (HTEs) is crucial for precision medicine. While multiple studies can improve the generalizability of results, leveraging them for estimation is statistically challenging. Existing approaches often assume identical HTEs across studies, but this may be violated due to various sources of between-study heterogeneity, including differences in study design, study populations, and data collection protocols, among others. To this end, we propose a framework for multi-study HTE estimation that accounts for between-study heterogeneity in the nuisance functions and treatment effects. Our approach, the multi-study R-learner, extends the R-learner to obtain principled statistical estimation with machine learning (ML) in the multi-study setting. It involves a data-adaptive objective function that links study-specific treatment effects with nuisance functions through membership probabilities, which enable information to be borrowed across potentially heterogeneous studies. The multi-study R-learner framework can combine data from randomized controlled trials, observational studies, or a combination of both. It's easy to implement and flexible in its ability to incorporate ML for estimating HTEs, nuisance functions, and membership probabilities. In the series estimation framework, we show that the multi-study R-learner is asymptotically normal and more efficient than the R-learner when there is between-study heterogeneity in the propensity score model under homoscedasticity. We illustrate using cancer data that the proposed method performs favorably compared to existing approaches in the presence of between-study heterogeneity.

Related papers

• Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift [46.84912148188679]
  Causal inference has the potential to improve the generalizability, transportability, and replicability of scientific findings. Existing data fusion methods focus on binary or continuous outcomes. We propose two novel approaches for multi-source causal survival analysis.
  arXiv  Detail & Related papers  (2025-01-30T23:21:25Z)
• Multi-CATE: Multi-Accurate Conditional Average Treatment Effect Estimation Robust to Unknown Covariate Shifts [12.289361708127876]
  We use methodology for learning multi-accurate predictors to post-process CATE T-learners. We show how this approach can combine (large) confounded observational and (smaller) randomized datasets.
  arXiv  Detail & Related papers  (2024-05-28T14:12:25Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Counterfactual Data Augmentation with Contrastive Learning [27.28511396131235]
  We introduce a model-agnostic data augmentation method that imputes the counterfactual outcomes for a selected subset of individuals. We use contrastive learning to learn a representation space and a similarity measure such that in the learned representation space close individuals identified by the learned similarity measure have similar potential outcomes. This property ensures reliable imputation of counterfactual outcomes for the individuals with close neighbors from the alternative treatment group.
  arXiv  Detail & Related papers  (2023-11-07T00:36:51Z)
• B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding [51.74479522965712]
  We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on hidden confounding. We prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods.
  arXiv  Detail & Related papers  (2023-04-20T18:07:19Z)
• Gaussian Latent Dirichlet Allocation for Discrete Human State Discovery [1.057079240576682]
  We propose and validate an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for the problem of discrete state discovery. GLDA borrows the individual-specific mixture structure from a popular topic model Latent Dirichlet Allocation (LDA) in Natural Language Processing. We found that in both datasets the GLDA-learned class weights achieved significantly higher correlations with clinically assessed depression, anxiety, and stress scores than those produced by the baseline GMM.
  arXiv  Detail & Related papers  (2022-06-28T18:33:46Z)
• Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records [1.3192560874022086]
  We investigate causal modelling of an RCT-established null causal association: the effect of antihypertensive use on incident cancer risk. We develop a dataset for our observational study and a Transformer-based model, Targeted BEHRT coupled with doubly robust estimation. We find that our model provides more accurate estimates of RR compared to benchmarks for risk ratio estimation on high-dimensional EHR.
  arXiv  Detail & Related papers  (2022-02-07T20:05:05Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability [0.158310730488265]
  A non-parametric framework is proposed that estimates the statistical significance of classifications using deep learning architectures. A label permutation test is proposed in both studies using cross-validation (CV) and resubstitution with upper bound correction (RUB) as validation methods. We found in the permutation test that CV and RUB methods offer a false positive rate close to the significance level and an acceptable statistical power.
  arXiv  Detail & Related papers  (2021-03-30T21:15:39Z)
• Estimating Structural Target Functions using Machine Learning and Influence Functions [103.47897241856603]
  We propose a new framework for statistical machine learning of target functions arising as identifiable functionals from statistical models. This framework is problem- and model-agnostic and can be used to estimate a broad variety of target parameters of interest in applied statistics. We put particular focus on so-called coarsening at random/doubly robust problems with partially unobserved information.
  arXiv  Detail & Related papers  (2020-08-14T16:48:29Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.