Disentangle Estimation of Causal Effects from Cross-Silo Data
- URL: http://arxiv.org/abs/2401.02154v1
- Date: Thu, 4 Jan 2024 09:05:37 GMT
- Title: Disentangle Estimation of Causal Effects from Cross-Silo Data
- Authors: Yuxuan Liu, Haozhao Wang, Shuang Wang, Zhiming He, Wenchao Xu,
Jialiang Zhu, Fan Yang
- Abstract summary: We introduce an innovative disentangle architecture designed to facilitate the seamless cross-silo transmission of model parameters.
We introduce global constraints into the equation to effectively mitigate bias within the various missing domains.
Our method outperforms state-of-the-art baselines.
- Score: 14.684584362172666
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Estimating causal effects among different events is of great importance to
critical fields such as drug development. Nevertheless, the data features
associated with events may be distributed across various silos and remain
private within respective parties, impeding direct information exchange between
them. This, in turn, can result in biased estimations of local causal effects,
which rely on the characteristics of only a subset of the covariates. To tackle
this challenge, we introduce an innovative disentangle architecture designed to
facilitate the seamless cross-silo transmission of model parameters, enriched
with causal mechanisms, through a combination of shared and private branches.
Besides, we introduce global constraints into the equation to effectively
mitigate bias within the various missing domains, thereby elevating the
accuracy of our causal effect estimation. Extensive experiments conducted on
new semi-synthetic datasets show that our method outperforms state-of-the-art
baselines.
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) - Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm [14.980926991441345]
We show that datasets containing interventional data can be effectively extracted under realistic assumptions about the data distribution.
We introduce interventional faithfulness, which relies on comparisons between the marginal distributions of each variable across observational and interventional settings.
We also introduce Intersort, an algorithm designed to infer the causal order from datasets containing large numbers of single-variable interventions.
arXiv Detail & Related papers (2024-05-28T16:07:17Z) - The Risk of Federated Learning to Skew Fine-Tuning Features and
Underperform Out-of-Distribution Robustness [50.52507648690234]
Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model.
We introduce three robustness indicators and conduct experiments across diverse robust datasets.
Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
arXiv Detail & Related papers (2024-01-25T09:18:51Z) - Variation-based Cause Effect Identification [5.744133015573047]
We propose a variation-based cause effect identification (VCEI) framework for causal discovery.
Our framework relies on the principle of independence of cause and mechanism (ICM) under the assumption of an existing acyclic causal link.
In the causal direction, such variations are expected to have no impact on the effect generation mechanism.
arXiv Detail & Related papers (2022-11-22T05:19:12Z) - Learning Infomax and Domain-Independent Representations for Causal
Effect Inference with Real-World Data [9.601837205635686]
We learn the Infomax and Domain-Independent Representations to solve the above puzzles.
We show that our method achieves state-of-the-art performance on causal effect inference.
arXiv Detail & Related papers (2022-02-22T13:35:15Z) - Learning Neural Causal Models with Active Interventions [83.44636110899742]
We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process.
Our method significantly reduces the required number of interactions compared with random intervention targeting.
We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
arXiv Detail & Related papers (2021-09-06T13:10:37Z) - Efficient Causal Inference from Combined Observational and
Interventional Data through Causal Reductions [68.6505592770171]
Unobserved confounding is one of the main challenges when estimating causal effects.
We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders.
We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
arXiv Detail & Related papers (2021-03-08T14:29:07Z) - Domain Adaptative Causality Encoder [52.779274858332656]
We leverage the characteristics of dependency trees and adversarial learning to address the tasks of adaptive causality identification and localisation.
We present a new causality dataset, namely MedCaus, which integrates all types of causality in the text.
arXiv Detail & Related papers (2020-11-27T04:14:55Z) - Learning Joint Nonlinear Effects from Single-variable Interventions in
the Presence of Hidden Confounders [9.196779204457059]
We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders.
We prove identifiability under the assumption that the data is generated from a nonlinear continuous structural causal model.
We also conduct comprehensive experiments to verify the identifiability result as well as to compare the performance of our approach against a baseline on both synthetic and real-world data.
arXiv Detail & Related papers (2020-05-23T12:52:09Z) - A Critical View of the Structural Causal Model [89.43277111586258]
We show that one can identify the cause and the effect without considering their interaction at all.
We propose a new adversarial training method that mimics the disentangled structure of the causal model.
Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
arXiv Detail & Related papers (2020-02-23T22:52:28Z)
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.