Federated Causal Inference from Observational Data
- URL: http://arxiv.org/abs/2308.13047v2
- Date: Thu, 30 May 2024 08:19:34 GMT
- Title: Federated Causal Inference from Observational Data
- Authors: Thanh Vinh Vo, Young lee, Tze-Yun Leong,
- Abstract summary: Decentralized data sources are prevalent in real-world applications, posing a formidable challenge for causal inference.
In this article, we propose a framework to estimate causal effects from decentralized data sources.
The proposed framework avoid exchanging raw data among the sources, thus contributing towards privacy-preserving causal learning.
- Score: 6.290787709927236
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Decentralized data sources are prevalent in real-world applications, posing a formidable challenge for causal inference. These sources cannot be consolidated into a single entity owing to privacy constraints. The presence of dissimilar data distributions and missing values within them can potentially introduce bias to the causal estimands. In this article, we propose a framework to estimate causal effects from decentralized data sources. The proposed framework avoid exchanging raw data among the sources, thus contributing towards privacy-preserving causal learning. Three instances of the proposed framework are introduced to estimate causal effects across a wide range of diverse scenarios within a federated setting. (1) FedCI: a Bayesian framework based on Gaussian processes for estimating causal effects from federated observational data sources. It estimates the posterior distributions of the causal effects to compute the higher-order statistics that capture the uncertainty. (2) CausalRFF: an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. It estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. (3) CausalFI: a new approach for federated causal inference from incomplete data, enabling the estimation of causal effects from multiple decentralized and incomplete data sources. It accounts for the missing data under the missing at random assumption, while also estimating higher-order statistics of the causal estimands. The proposed federated framework and its instances are an important step towards a privacy-preserving causal learning model.
Related papers
- Do Finetti: On Causal Effects for Exchangeable Data [45.96632286841583]
We study causal effect estimation in a setting where the data are not i.i.d.
We focus on exchangeable data satisfying an assumption of independent causal mechanisms.
arXiv Detail & Related papers (2024-05-29T07:31:18Z) - Federated Causal Discovery from Heterogeneous Data [70.31070224690399]
We propose a novel FCD method attempting to accommodate arbitrary causal models and heterogeneous data.
These approaches involve constructing summary statistics as a proxy of the raw data to protect data privacy.
We conduct extensive experiments on synthetic and real datasets to show the efficacy of our method.
arXiv Detail & Related papers (2024-02-20T18:53:53Z) - Approximating Counterfactual Bounds while Fusing Observational, Biased
and Randomised Data Sources [64.96984404868411]
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies.
We show that the likelihood of the available data has no local maxima.
We then show how the same approach can address the general case of multiple datasets.
arXiv Detail & Related papers (2023-07-31T11:28:24Z) - An Adaptive Kernel Approach to Federated Learning of Heterogeneous
Causal Effects [10.248235276871256]
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources.
We introduce an adaptive transfer algorithm that learns the similarities among the data sources.
The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.
arXiv Detail & Related papers (2023-01-01T04:57:48Z) - Federated Causal Discovery [74.37739054932733]
This paper develops a gradient-based learning framework named DAG-Shared Federated Causal Discovery (DS-FCD)
It can learn the causal graph without directly touching local data and naturally handle the data heterogeneity.
Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method.
arXiv Detail & Related papers (2021-12-07T08:04:12Z) - BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable.
We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
arXiv Detail & Related papers (2021-06-07T10:14:18Z) - Federated Estimation of Causal Effects from Observational Data [19.657789891394504]
We present a novel framework for causal inference with federated data sources.
We assess and integrate local causal effects from different private data sources without centralizing them.
arXiv Detail & Related papers (2021-05-31T08:06:00Z) - Latent Causal Invariant Model [128.7508609492542]
Current supervised learning can learn spurious correlation during the data-fitting process.
We propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
arXiv Detail & Related papers (2020-11-04T10:00:27Z) - 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)
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.