Extended fiducial inference for individual treatment effects via deep neural networks

Authors

Faming Liang (ORCID: https://orcid.org/0000-0002-1177-5501)
Sehwan Kim (ORCID: https://orcid.org/0000-0002-9219-1056)

Institutions

Purdue University West Lafayette (US)

Publication Details

Journal: Statistics and Computing
Published: 2025-05-17
DOI: https://doi.org/10.1007/s11222-025-10624-8
Primary Topic: Advanced Causal Inference Techniques
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Science Foundation
National Institutes of Health

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article

Extended fiducial inference for individual treatment effects via deep neural networks

Faming Liang, Sehwan Kim

May 17, 2025

Statistics and Computing

Advanced Causal Inference Techniques

article

Extended fiducial inference for individual treatment effects via deep neural networks

Faming Liang, Sehwan Kim

May 17, 2025article en

Abstract

No abstract available for this paper.

Statistics and ComputingVol. 35(4)

Purdue University West Lafayette (US)

National Science Foundation, National Institutes of Health

Openalex Percentile: Top 89%

Advanced Causal Inference Techniques

DOI Open Access PDF OpenAlex

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Extended fiducial inference for individual treatment effects via deep neural networks — Faming Liang, Sehwan Kim · Statistics and Computing (2025) | TGRS Research Map | TGRS