Adaptive Multi-Source Causal Inference from Observational Data

Vo, Thanh Vinh; Wei, Pengfei; Hoang, Trong Nghia; Leong, Tze-Yun

doi:10.1145/3511808.3557230

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…HTCE [20] aids in estimating causal effects in the target domain with assistance from source domain data, but it is limited to specific source and target domains. FedCI [21] and Causal-RFF [22] primarily focus on scenarios where different parties have the same data feature dimensions. In summary, research on cross-silo causal inference accounting for heterogeneous feature dimensions remains unexplored as of now.…”

Section: Introductionmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Section: Introductionmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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“…Our work is most closely related to the recent studies of privacy‐preserving methods for causal inference by Vo et al 27 and Han et al 28,29 †† Vo et al 27 estimate treatment effects by modeling potential outcomes by Gaussian processes. Han et al 28,29 propose to estimate treatment effects for target populations by adaptively and optimally weighing source populations, accounting for the risk of negative transfer when source and target populations are heterogeneous.…”

Section: Introductionmentioning

confidence: 79%

Federated causal inference in heterogeneous observational data

Xiong

Koenecke

Powell

et al. 2023

Statistics in Medicine

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We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individual‐level data cannot be shared across sites; the sites may also have heterogeneous populations and treatment assignment mechanisms. Motivated by these considerations, we develop federated methods to draw inferences on the average treatment effects of combined data across sites. Our methods first compute summary statistics locally using propensity scores and then aggregate these statistics across sites to obtain point and variance estimators of average treatment effects. We show that these estimators are consistent and asymptotically normal. To achieve these asymptotic properties, we find that the aggregation schemes need to account for the heterogeneity in treatment assignments and in outcomes across sites. We demonstrate the validity of our federated methods through a comparative study of two large medical claims databases.

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“…federated estimation [50] (not included in the scoping review final selection since the paper was not published when the scoping review search was launched)…”

Section: Methodsmentioning

confidence: 99%

Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Healthcare

Lemyre,

Lévesque,

Domingue

et al. 2023

Preprint

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Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for healthcare research frameworks. This paper aims to support researchers and data custodians in three ways: (1) providing a concise overview of the literature on statistical inference methods for horizontally partitioned data; (2) describing the methods applicable to generalized linear models (GLM) and assessing their underlying distributional assumptions; (3) adapting existing methods to make them fully usable in healthcare research. A scoping review methodology was employed for the literature mapping, from which methods presenting a methodological framework for GLM analyses with horizontally partitioned data were identified and assessed from the perspective of applicability in healthcare research. From the review, 41 articles were selected, and six approaches were extracted for conducting standard GLM-based statistical analysis. However, these approaches assumed evenly and identically distributed data across nodes. Consequently, statistical procedures were derived to accommodate uneven node sample sizes and heterogeneous data distributions across nodes. Workflows and detailed algorithms were developed to highlight information-sharing requirements and operational complexity.

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Adaptive Multi-Source Causal Inference from Observational Data

Cited by 5 publications

References 27 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

Federated causal inference in heterogeneous observational data

Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Healthcare

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