Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks

• URL: http://arxiv.org/abs/2605.10590v1
• Date: Mon, 11 May 2026 13:59:22 GMT
• Title: Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
• Abstract summary: Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding.<n>Existing methods for causal sensitivity analysis are per-instance procedures, meaning that changes to the dataset, causal query, sensitivity level, or treatment require new computation.<n>Here, we propose an amortized approach to causal sensitivity analysis based on prior-data fitted networks.
• Score: 43.927835161998026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods for causal sensitivity analysis are per-instance procedures, meaning that changes to the dataset, causal query, sensitivity level, or treatment require new computation. Here, we instead present an in-context learning approach. Specifically, we propose an amortized approach to causal sensitivity analysis based on prior-data fitted networks. A key challenge is that the sensitivity bounds are not directly available when sampling training data. To address this, we develop a general prior-data construction that is applicable across the class of generalized treatment sensitivity models. Our construction involves a Lagrangian scalarization of the objective to generate training labels for the bounds through a tradeoff between causal effect min/max-imization and sensitivity model violation, which avoids model-specific analytical derivations. We further show that, under standard convexity and linearity conditions, our objective recovers the full Pareto frontier of solutions. Empirically, we demonstrate our amortized approach across various datasets, causal queries, and sensitivity levels, where our approach achieves a test-time computation that is orders of magnitude faster than per-instance methods. To the best of our knowledge, ours is the first foundation model for in-context learning for causal sensitivity analysis.

Related papers

• Preference Learning for AI Alignment: a Causal Perspective [55.2480439325792]
  We frame this problem in a causal paradigm, providing the rich toolbox of causality to identify persistent challenges.<n>Inheriting from the literature of causal inference, we identify key assumptions necessary for reliable generalisation.<n>We illustrate failure modes of naive reward models and demonstrate how causally-inspired approaches can improve model robustness.
  arXiv  Detail & Related papers  (2025-06-06T10:45:42Z)
• Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models [0.5223954072121659]
  Contaminant observations and outliers often cause problems when estimating the parameters of cognitive models.<n>In this study, we test and improve the robustness of parameter estimation using amortized Bayesian inference.<n>The proposed method is straightforward and practical to implement and has a broad applicability in fields where outlier detection or removal is challenging.
  arXiv  Detail & Related papers  (2024-12-29T21:22:24Z)
• Adaptive Sensitivity Analysis for Robust Augmentation against Natural Corruptions in Image Segmentation [21.609070498399863]
  We present an adaptive, sensitivity-guided augmentation method to enhance robustness against natural corruptions.<n>Our sensitivity analysis on average runs 10x faster and requires about 200x less storage than previous sensitivity analysis.<n>With minimal fine-tuning, our sensitivity-guided augmentation method achieves improved robustness on both real-world and synthetic datasets.
  arXiv  Detail & Related papers  (2024-06-03T15:25:45Z)
• A Neural Framework for Generalized Causal Sensitivity Analysis [78.71545648682705]
  We propose NeuralCSA, a neural framework for causal sensitivity analysis. We provide theoretical guarantees that NeuralCSA is able to infer valid bounds on the causal query of interest.
  arXiv  Detail & Related papers  (2023-11-27T17:40:02Z)
• Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks [72.51255282371805]
  We prove a privacy bound for the KL divergence between model distributions on worst-case neighboring datasets. We find that this KL privacy bound is largely determined by the expected squared gradient norm relative to model parameters during training.
  arXiv  Detail & Related papers  (2023-10-31T16:13:22Z)
• The Memory Perturbation Equation: Understanding Model's Sensitivity to Data [16.98312108418346]
  We present the Memory-Perturbation Equation (MPE) which relates model's sensitivity to perturbation in its training data. Our empirical results show that sensitivity estimates obtained during training can be used to faithfully predict generalization on unseen test data.
  arXiv  Detail & Related papers  (2023-10-30T05:12:24Z)
• Sensitivity-Aware Amortized Bayesian Inference [8.753065246797561]
  Sensitivity analyses reveal the influence of various modeling choices on the outcomes of statistical analyses. We propose sensitivity-aware amortized Bayesian inference (SA-ABI), a multifaceted approach to integrate sensitivity analyses into simulation-based inference with neural networks. We demonstrate the effectiveness of our method in applied modeling problems, ranging from disease outbreak dynamics and global warming thresholds to human decision-making.
  arXiv  Detail & Related papers  (2023-10-17T10:14:10Z)
• Sharp Bounds for Generalized Causal Sensitivity Analysis [30.77874108094485]
  We propose a unified framework for causal sensitivity analysis under unobserved confounding. This includes (conditional) average treatment effects, effects for mediation analysis and path analysis, and distributional effects. Our bounds for (conditional) average treatment effects coincide with recent optimality results for causal sensitivity analysis.
  arXiv  Detail & Related papers  (2023-05-26T14:44:32Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• 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)

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.