Multiply Robust Federated Estimation of Targeted Average Treatment Effects

• URL: http://arxiv.org/abs/2309.12600v1
• Date: Fri, 22 Sep 2023 03:15:08 GMT
• Title: Multiply Robust Federated Estimation of Targeted Average Treatment Effects
• Authors: Larry Han and Zhu Shen and Jose Zubizarreta
• Abstract summary: We propose a novel approach to derive valid causal inferences for a target population using multi-site data. Our methodology incorporates transfer learning to estimate ensemble weights to combine information from source sites.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated or multi-site studies have distinct advantages over single-site studies, including increased generalizability, the ability to study underrepresented populations, and the opportunity to study rare exposures and outcomes. However, these studies are challenging due to the need to preserve the privacy of each individual's data and the heterogeneity in their covariate distributions. We propose a novel federated approach to derive valid causal inferences for a target population using multi-site data. We adjust for covariate shift and covariate mismatch between sites by developing multiply-robust and privacy-preserving nuisance function estimation. Our methodology incorporates transfer learning to estimate ensemble weights to combine information from source sites. We show that these learned weights are efficient and optimal under different scenarios. We showcase the finite sample advantages of our approach in terms of efficiency and robustness compared to existing approaches.

Related papers

• Multi-Source Conformal Inference Under Distribution Shift [41.701790856201036]
  We consider the problem of obtaining distribution-free prediction intervals for a target population, leveraging multiple potentially biased data sources. We derive the efficient influence functions for the quantiles of unobserved outcomes in the target and source populations. We propose a data-adaptive strategy to upweight informative data sources for efficiency gain and downweight non-informative data sources for bias reduction.
  arXiv  Detail & Related papers  (2024-05-15T13:33:09Z)
• The Curse of Diversity in Ensemble-Based Exploration [7.209197316045156]
  Training a diverse ensemble of data-sharing agents can significantly impair the performance of the individual ensemble members. We name this phenomenon the curse of diversity. We demonstrate the potential of representation learning to counteract the curse of diversity.
  arXiv  Detail & Related papers  (2024-05-07T14:14:50Z)
• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Probabilistic Contrastive Learning for Long-Tailed Visual Recognition [78.70453964041718]
  Longtailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. We propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space.
  arXiv  Detail & Related papers  (2024-03-11T13:44:49Z)
• Collaborative causal inference on distributed data [7.293479909193382]
  We propose a data collaboration quasi-experiment (DC-QE) that resolves the lack of both subjects and covariates, reducing random errors and biases in the estimation. Our method involves constructing dimensionality-reduced intermediate representations from private data from local parties, sharing intermediate representations instead of private data for privacy preservation, estimating propensity scores from the shared intermediate representations, and finally, estimating the treatment effects from propensity scores.
  arXiv  Detail & Related papers  (2022-08-16T18:28:56Z)
• Assaying Out-Of-Distribution Generalization in Transfer Learning [103.57862972967273]
  We take a unified view of previous work, highlighting message discrepancies that we address empirically. We fine-tune over 31k networks, from nine different architectures in the many- and few-shot setting.
  arXiv  Detail & Related papers  (2022-07-19T12:52:33Z)
• Decentralized Distributed Learning with Privacy-Preserving Data Synthesis [9.276097219140073]
  In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. Recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support diagnosis and prognosis. We present a decentralized distributed method that integrates features from local nodes, providing models able to generalize across multiple datasets while maintaining privacy.
  arXiv  Detail & Related papers  (2022-06-20T23:49:38Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging [8.984706828657814]
  Federated learning enables multiple institutions to collaboratively train machine learning models on their local data in a privacy-preserving way. We investigate the deleterious impact of a taxonomy of data heterogeneity regimes on federated learning methods, including quantity skew, label distribution skew, and imaging acquisition skew. We present several mitigation strategies to overcome performance drops from data heterogeneity, including weighted average for data quantity skew, weighted loss and batch normalization averaging for label distribution skew.
  arXiv  Detail & Related papers  (2021-07-18T05:47:48Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)
• Learning Diverse Representations for Fast Adaptation to Distribution Shift [78.83747601814669]
  We present a method for learning multiple models, incorporating an objective that pressures each to learn a distinct way to solve the task. We demonstrate our framework's ability to facilitate rapid adaptation to distribution shift.
  arXiv  Detail & Related papers  (2020-06-12T12:23:50Z)

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.