Distributional Causal Mediation via Conditional Generative Modeling

• URL: http://arxiv.org/abs/2605.01765v1
• Date: Sun, 03 May 2026 07:57:41 GMT
• Title: Distributional Causal Mediation via Conditional Generative Modeling
• Abstract summary: Distributional Causal Mediation Analysis (DCMA) is a generative learning framework for identifying and estimating treatment effects on entire outcome distributions transmitted through multiple mediators.<n>It reconstructs interventional outcome distributions via Monte Carlo forward simulation by noise resampling.<n> analytical error bounds are derived to decompose how estimation errors in the learned conditional models propagate to the reconstructed interventional outcome distributions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional changes induced by complex and nonlinear causal mechanisms. We propose Distributional Causal Mediation Analysis (DCMA), a generative learning framework for identifying and estimating treatment effects on entire outcome distributions transmitted through multiple mediators. DCMA learns conditional generative models for the mediators and the outcome, recovering the relevant conditional distributions from observational data. Leveraging the identification formulas, it reconstructs interventional outcome distributions via Monte Carlo forward simulation by noise resampling, enabling the capture of both classical summary effects and rich distributional contrasts such as energy distance and the Wasserstein distance. Analytical error bounds are derived to decompose how estimation errors in the learned conditional models propagate to the reconstructed interventional outcome distributions. The empirical effectiveness of DCMA is demonstrated through numerical experiments and real-world data applications.

Related papers

• Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference [12.531769483039405]
  We propose a causality-encoded diffusion framework that incorporates a known directed acyclic graph.<n>The resulting sampler approximately recovers the observational distribution and enables interventional sampling.<n>An application to flow data demonstrates practical utility of the proposed method in assessing disputed signalling linkages.
  arXiv  Detail & Related papers  (2026-04-23T16:32:49Z)
• Controllable Generative Sandbox for Causal Inference [9.416664327739516]
  CausalMix is a variational generative framework for causal inference.<n>It achieves state-of-the-art distributional metrics on mixed-type tables while providing stable, fine-grained causal control.<n>We demonstrate practical utility in a comparative safety study of metastatic castration-resistant prostate cancer treatments.
  arXiv  Detail & Related papers  (2026-03-03T23:37:05Z)
• Score-based diffusion models for diffuse optical tomography with uncertainty quantification [0.8443238959374133]
  We introduce a novel regularization approach that prevents overfitting of the score function by constructing a mixed score composed of a learned and a model-based component.<n>Experiments demonstrate that a data-driven prior distribution results in posterior samples with low variance, compared to classical model-based estimation.
  arXiv  Detail & Related papers  (2026-02-03T12:14:07Z)
• Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship [54.575090553659074]
  We develop an algorithm that efficiently uncovers causal relationships with quadratic complexity in the number of observational variables.<n>Our experiments on a varied benchmark of large-scale datasets show superior or equivalent performance compared to existing works.
  arXiv  Detail & Related papers  (2026-02-03T10:26:16Z)
• Spatial Reasoning with Denoising Models [49.83744014336816]
  We introduce a framework to perform reasoning over sets of continuous variables via denoising generative models.<n>For the first time, that order of generation can successfully be predicted by the denoising network itself.<n>Using these findings, we can increase the accuracy of specific reasoning tasks from 1% to >50%.
  arXiv  Detail & Related papers  (2025-02-28T14:08:30Z)
• Identifiable Latent Neural Causal Models [82.14087963690561]
  Causal representation learning seeks to uncover latent, high-level causal representations from low-level observed data. We determine the types of distribution shifts that do contribute to the identifiability of causal representations. We translate our findings into a practical algorithm, allowing for the acquisition of reliable latent causal representations.
  arXiv  Detail & Related papers  (2024-03-23T04:13:55Z)
• Ensemble Modeling for Multimodal Visual Action Recognition [50.38638300332429]
  We propose an ensemble modeling approach for multimodal action recognition. We independently train individual modality models using a variant of focal loss tailored to handle the long-tailed distribution of the MECCANO [21] dataset.
  arXiv  Detail & Related papers  (2023-08-10T08:43:20Z)
• On Error Propagation of Diffusion Models [77.91480554418048]
  We develop a theoretical framework to mathematically formulate error propagation in the architecture of DMs. We apply the cumulative error as a regularization term to reduce error propagation. Our proposed regularization reduces error propagation, significantly improves vanilla DMs, and outperforms previous baselines.
  arXiv  Detail & Related papers  (2023-08-09T15:31:17Z)
• De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network [5.465397606401007]
  Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. We propose a de-confounding representation learning (DRL) framework for counterfactual outcome estimation of continuous treatment. We show that the DRL model performs superiorly in learning de-confounding representations and outperforms state-of-the-art counterfactual inference models for continuous treatment variables.
  arXiv  Detail & Related papers  (2023-07-24T08:56:25Z)
• Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data [68.62134204367668]
  This paper studies score approximation, estimation, and distribution recovery of diffusion models, when data are supported on an unknown low-dimensional linear subspace. We show that with a properly chosen neural network architecture, the score function can be both accurately approximated and efficiently estimated. The generated distribution based on the estimated score function captures the data geometric structures and converges to a close vicinity of the data distribution.
  arXiv  Detail & Related papers  (2023-02-14T17:02:35Z)
• Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization [89.73665256847858]
  We show that out-of-distribution performance is strongly correlated with in-distribution performance for a wide range of models and distribution shifts. Specifically, we demonstrate strong correlations between in-distribution and out-of-distribution performance on variants of CIFAR-10 & ImageNet. We also investigate cases where the correlation is weaker, for instance some synthetic distribution shifts from CIFAR-10-C and the tissue classification dataset Camelyon17-WILDS.
  arXiv  Detail & Related papers  (2021-07-09T19:48:23Z)
• Harmonization with Flow-based Causal Inference [12.739380441313022]
  This paper presents a normalizing-flow-based method to perform counterfactual inference upon a structural causal model (SCM) to harmonize medical data. We evaluate on multiple, large, real-world medical datasets to observe that this method leads to better cross-domain generalization compared to state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-06-12T19:57:35Z)
• Counterfactual Maximum Likelihood Estimation for Training Deep Networks [83.44219640437657]
  Deep learning models are prone to learning spurious correlations that should not be learned as predictive clues. We propose a causality-based training framework to reduce the spurious correlations caused by observable confounders. We conduct experiments on two real-world tasks: Natural Language Inference (NLI) and Image Captioning.
  arXiv  Detail & Related papers  (2021-06-07T17:47:16Z)

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.