Causal Inference with Conditional Front-Door Adjustment and Identifiable
Variational Autoencoder
- URL: http://arxiv.org/abs/2310.01937v1
- Date: Tue, 3 Oct 2023 10:24:44 GMT
- Title: Causal Inference with Conditional Front-Door Adjustment and Identifiable
Variational Autoencoder
- Authors: Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Kui Yu
- Abstract summary: Front-door adjustment is a practical approach for dealing with unobserved confounding variables.
We develop the theorem that guarantees the causal effect identifiability of CFD adjustment.
We propose CFDiVAE to learn the representation of the CFD adjustment variable directly from data.
- Score: 28.94606676886985
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: An essential and challenging problem in causal inference is causal effect
estimation from observational data. The problem becomes more difficult with the
presence of unobserved confounding variables. The front-door adjustment is a
practical approach for dealing with unobserved confounding variables. However,
the restriction for the standard front-door adjustment is difficult to satisfy
in practice. In this paper, we relax some of the restrictions by proposing the
concept of conditional front-door (CFD) adjustment and develop the theorem that
guarantees the causal effect identifiability of CFD adjustment. Furthermore, as
it is often impossible for a CFD variable to be given in practice, it is
desirable to learn it from data. By leveraging the ability of deep generative
models, we propose CFDiVAE to learn the representation of the CFD adjustment
variable directly from data with the identifiable Variational AutoEncoder and
formally prove the model identifiability. Extensive experiments on synthetic
datasets validate the effectiveness of CFDiVAE and its superiority over
existing methods. The experiments also show that the performance of CFDiVAE is
less sensitive to the causal strength of unobserved confounding variables. We
further apply CFDiVAE to a real-world dataset to demonstrate its potential
application.
Related papers
- 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) - Efficient Conformal Prediction under Data Heterogeneity [79.35418041861327]
Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification.
Existing approaches for tackling non-exchangeability lead to methods that are not computable beyond the simplest examples.
This work introduces a new efficient approach to CP that produces provably valid confidence sets for fairly general non-exchangeable data distributions.
arXiv Detail & Related papers (2023-12-25T20:02:51Z) - Boosted Control Functions [10.503777692702952]
This work aims to bridge the gap between causal effect estimation and prediction tasks.
We establish a novel connection between the field of distribution from machine learning, and simultaneous equation models and control function from econometrics.
Within this framework, we propose a strong notion of invariance for a predictive model and compare it with existing (weaker) versions.
arXiv Detail & Related papers (2023-10-09T15:43:46Z) - EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for
Industrial IoT [0.0]
We propose the Drift-Aware Weight Consolidation (DAWC) to mitigate the challenges posed by frequent data drift in the industrial Internet of Things (IIoT)
DAWC efficiently manages multiple data drift scenarios, minimizing the need for constant model fine-tuning on edge devices.
We have also developed a comprehensive diagnosis and visualization platform.
arXiv Detail & Related papers (2023-10-07T06:48:07Z) - Flexible and Robust Counterfactual Explanations with Minimal Satisfiable
Perturbations [56.941276017696076]
We propose a conceptually simple yet effective solution named Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP)
CEMSP constrains changing values of abnormal features with the help of their semantically meaningful normal ranges.
Compared to existing methods, we conduct comprehensive experiments on both synthetic and real-world datasets to demonstrate that our method provides more robust explanations while preserving flexibility.
arXiv Detail & Related papers (2023-09-09T04:05:56Z) - Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
We propose a framework for efficient and effective counterfactual inference implemented with neural networks.
The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data.
Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
arXiv Detail & Related papers (2023-06-09T08:30:51Z) - Federated Conformal Predictors for Distributed Uncertainty
Quantification [83.50609351513886]
Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning.
In this paper, we extend conformal prediction to the federated learning setting.
We propose a weaker notion of partial exchangeability, better suited to the FL setting, and use it to develop the Federated Conformal Prediction framework.
arXiv Detail & Related papers (2023-05-27T19:57:27Z) - Causal Effect Estimation with Variational AutoEncoder and the Front Door
Criterion [23.20371860838245]
The front-door criterion is often difficult to identify the set of variables used for front-door adjustment from data.
By leveraging the ability of deep generative models in representation learning, we propose FDVAE to learn the representation of a Front-Door adjustment set with a Variational AutoEncoder.
arXiv Detail & Related papers (2023-04-24T10:04:28Z) - Diffusion Causal Models for Counterfactual Estimation [18.438307666925425]
We consider the task of counterfactual estimation from observational imaging data given a known causal structure.
We propose Diff-SCM, a deep structural causal model that builds on recent advances of generative energy-based models.
We find that Diff-SCM produces more realistic and minimal counterfactuals than baselines on MNIST data and can also be applied to ImageNet data.
arXiv Detail & Related papers (2022-02-21T12:23:01Z) - Variational Causal Networks: Approximate Bayesian Inference over Causal
Structures [132.74509389517203]
We introduce a parametric variational family modelled by an autoregressive distribution over the space of discrete DAGs.
In experiments, we demonstrate that the proposed variational posterior is able to provide a good approximation of the true posterior.
arXiv Detail & Related papers (2021-06-14T17:52:49Z) - Learning Disentangled Latent Factors from Paired Data in Cross-Modal
Retrieval: An Implicit Identifiable VAE Approach [33.61751393224223]
We deal with the problem of learning the underlying disentangled latent factors that are shared between the paired bi-modal data in cross-modal retrieval.
We propose a novel idea of the implicit decoder, which completely removes the ambient data decoding module from a latent variable model.
Our model is shown to identify the factors accurately, significantly outperforming conventional encoder-decoder latent variable models.
arXiv Detail & Related papers (2020-12-01T17:47:50Z)
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.