Cover Image

Collaborative causal inference on distributed data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kawamata, Yuji, Motai, Ryoki, Okada, Yukihiko, Imakura, Akira, Sakurai, Tetsuya
Format:	Preprint
Published:	2022
Subjects:	Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Estimation of conditional average treatment effects on distributed confidential data
by: Kawamata, Yuji, et al.
Published: (2024)

Estimating Covariate-balanced Survival Curve in Distributed Data Environment using Data Collaboration Quasi-Experiment
by: Toyoda, Akihiro, et al.
Published: (2025)

Data collaboration for causal inference from limited medical testing and medication data
by: Nakayama, Tomoru, et al.
Published: (2025)

A new type of federated clustering: A non-model-sharing approach
by: Kawamata, Yuji, et al.
Published: (2025)

Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach
by: Mashiko, Sota, et al.
Published: (2025)

FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
by: Imakura, Akira, et al.
Published: (2024)

Covariance-Based Structural Equation Modeling in Small-Sample Settings with $p>n$
by: Hasegawa, Hiroki, et al.
Published: (2026)

Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data
by: Sugawara, Sota, et al.
Published: (2026)

LSEC: Large-scale spectral ensemble clustering
by: Li, Hongmin, et al.
Published: (2021)

Formalising causal inference as prediction on a target population
by: Höltgen, Benedikt, et al.
Published: (2024)

Inverting estimating equations for causal inference on quantiles
by: Cheng, Chao, et al.
Published: (2024)

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
by: Blöbaum, Patrick, et al.
Published: (2022)

Identifiable causal inference with noisy treatment and no side information
by: Pöllänen, Antti, et al.
Published: (2023)

Nonparametric efficient inference for network quantile causal effects under partial interference
by: Cheng, Chao, et al.
Published: (2026)

Predictive variational inference: Learn the predictively optimal posterior distribution
by: Lai, Jinlin, et al.
Published: (2024)

An AI-powered Bayesian generative modeling approach for causal inference in observational studies
by: Liu, Qiao, et al.
Published: (2025)

Diffusion posterior sampling for simulation-based inference in tall data settings
by: Linhart, Julia, et al.
Published: (2024)

Amortized Bayesian inference for actigraph time sheet data from mobile devices
by: Zhou, Daniel, et al.
Published: (2026)

IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
by: Johansson, Fredrik D.
Published: (2024)

Dynamical causality under invisible confounders
by: Yan, Jinling, et al.
Published: (2024)

Statistical inference for case-control logistic regression via integrating external summary data
by: Shi, Hengchao, et al.
Published: (2024)

Convolutional neural networks for valid and efficient causal inference
by: Ghasempour, Mohammad, et al.
Published: (2023)

Clustered random forests with correlated data for optimal estimation and inference under potential covariate shift
by: Young, Elliot H., et al.
Published: (2025)

Targeting relative risk heterogeneity with causal forests
by: Shirvaikar, Vik, et al.
Published: (2023)

Separation-based distance measures for causal graphs
by: Wahl, Jonas, et al.
Published: (2024)

Out-of-distribution robustness for multivariate analysis via causal regularisation
by: Durand, Homer, et al.
Published: (2024)

Mixture models for data with unknown distributions
by: Newman, M. E. J.
Published: (2025)

Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation
by: Cho, Soonhong, et al.
Published: (2024)

Assumption violations in causal discovery and the robustness of score matching
by: Montagna, Francesco, et al.
Published: (2023)

Controlling for discrete unmeasured confounding in nonlinear causal models
by: Burauel, Patrick, et al.
Published: (2024)

Transductive conformal inference with adaptive scores
by: Gazin, Ulysse, et al.
Published: (2023)

Valid causal inference with unobserved confounding in high-dimensional settings
by: Moosavi, Niloofar, et al.
Published: (2024)

Semi-parametric inference based on adaptively collected data
by: Lin, Licong, et al.
Published: (2023)

Multi-task learning via robust regularized clustering with non-convex group penalties
by: Okazaki, Akira, et al.
Published: (2024)

C-XGBoost: A tree boosting model for causal effect estimation
by: Kiriakidou, Niki, et al.
Published: (2024)

Cross-validating causal discovery via Leave-One-Variable-Out
by: Schkoda, Daniela, et al.
Published: (2024)

Bootstrap aggregation and confidence measures to improve time series causal discovery
by: Debeire, Kevin, et al.
Published: (2023)

When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer
by: Hector, Emily C., et al.
Published: (2024)

Cluster weighted models with multivariate skewed distributions for functional data
by: Anton, Cristina, et al.
Published: (2025)

Assessing the overall and partial causal well-specification of nonlinear additive noise models
by: Schultheiss, Christoph, et al.
Published: (2023)