Causal Optimal Transport of Abstractions

• URL: http://arxiv.org/abs/2312.08107v1
• Date: Wed, 13 Dec 2023 12:54:34 GMT
• Title: Causal Optimal Transport of Abstractions
• Authors: Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini and Theodoros Damoulas
• Abstract summary: Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity. We propose COTA, the first method to learn abstraction maps from observational and interventional data without assuming complete knowledge of the underlying SCMs. We extensively evaluate COTA on synthetic and real world problems, and showcase its advantages over non-causal, independent and aggregated COTA formulations.
• Score: 8.642152250082368
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them. These maps have significant relevance for real-world challenges such as synthesizing causal evidence from multiple experimental environments, learning causally consistent representations at different resolutions, and linking interventions across multiple SCMs. In this work, we propose COTA, the first method to learn abstraction maps from observational and interventional data without assuming complete knowledge of the underlying SCMs. In particular, we introduce a multi-marginal Optimal Transport (OT) formulation that enforces do-calculus causal constraints, together with a cost function that relies on interventional information. We extensively evaluate COTA on synthetic and real world problems, and showcase its advantages over non-causal, independent and aggregated COTA formulations. Finally, we demonstrate the efficiency of our method as a data augmentation tool by comparing it against the state-of-the-art CA learning framework, which assumes fully specified SCMs, on a real-world downstream task.

Related papers

• Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities [16.69453837626083]
  We propose a Correlation-decoupled Knowledge Distillation (CorrKD) framework for the Multimodal Sentiment Analysis (MSA) task under uncertain missing modalities. We present a sample-level contrastive distillation mechanism that transfers comprehensive knowledge containing cross-sample correlations to reconstruct missing semantics. We design a response-disentangled consistency distillation strategy to optimize the sentiment decision boundaries of the student network.
  arXiv  Detail & Related papers  (2024-04-25T09:35:09Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• SSL Framework for Causal Inconsistency between Structures and Representations [23.035761299444953]
  Cross-pollination of deep learning and causal discovery has catalyzed a burgeoning field of research seeking to elucidate causal relationships within non-statistical data forms like images, videos, and text. We theoretically develop intervention strategies suitable for indefinite data and derive causal consistency condition (CCC) CCC could potentially play an influential role in various fields.
  arXiv  Detail & Related papers  (2023-10-28T08:29:49Z)
• Towards Causal Foundation Model: on Duality between Causal Inference and Attention [18.046388712804042]
  We take a first step towards building causally-aware foundation models for treatment effect estimations. We propose a novel, theoretically justified method called Causal Inference with Attention (CInA)
  arXiv  Detail & Related papers  (2023-10-01T22:28:34Z)
• Inducing Causal Structure for Abstractive Text Summarization [76.1000380429553]
  We introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.
  arXiv  Detail & Related papers  (2023-08-24T16:06:36Z)
• Learning a Structural Causal Model for Intuition Reasoning in Conversation [20.243323155177766]
  Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models. We develop a conversation cognitive model ( CCM) that explains how each utterance receives and activates channels of information. By leveraging variational inference, it explores substitutes for implicit causes, addresses the issue of their unobservability, and reconstructs the causal representations of utterances through the evidence lower bounds.
  arXiv  Detail & Related papers  (2023-05-28T13:54:09Z)
• Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions [8.767175335575386]
  An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution. We introduce a first framework for causal abstraction learning between SCMs based on the formalization of abstraction recently proposed by Rischel.
  arXiv  Detail & Related papers  (2023-01-14T11:22:16Z)
• Differentiable Invariant Causal Discovery [106.87950048845308]
  Learning causal structure from observational data is a fundamental challenge in machine learning. This paper proposes Differentiable Invariant Causal Discovery (DICD) to avoid learning spurious edges and wrong causal directions. Extensive experiments on synthetic and real-world datasets verify that DICD outperforms state-of-the-art causal discovery methods up to 36% in SHD.
  arXiv  Detail & Related papers  (2022-05-31T09:29:07Z)
• Partial Counterfactual Identification from Observational and Experimental Data [83.798237968683]
  We develop effective Monte Carlo algorithms to approximate the optimal bounds from an arbitrary combination of observational and experimental data. Our algorithms are validated extensively on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-10-12T02:21:30Z)
• 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)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)

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.