Learning Causal Effects on Hypergraphs

• URL: http://arxiv.org/abs/2207.04049v1
• Date: Thu, 7 Jul 2022 23:14:10 GMT
• Title: Learning Causal Effects on Hypergraphs
• Authors: Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, Jaime Teevan
• Abstract summary: We study hypergraphs from the perspective of causality. In this work, we investigate high-order interference modeling, and propose a new causality learning framework powered by hypergraph neural networks.
• Score: 33.41001768564952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existing studies which leverage statistical dependencies, we study hypergraphs from the perspective of causality. Specifically, in this paper, we focus on the problem of individual treatment effect (ITE) estimation on hypergraphs, aiming to estimate how much an intervention (e.g., wearing face covering) would causally affect an outcome (e.g., COVID-19 infection) of each individual node. Existing works on ITE estimation either assume that the outcome on one individual should not be influenced by the treatment assignments on other individuals (i.e., no interference), or assume the interference only exists between pairs of connected individuals in an ordinary graph. We argue that these assumptions can be unrealistic on real-world hypergraphs, where higher-order interference can affect the ultimate ITE estimations due to the presence of group interactions. In this work, we investigate high-order interference modeling, and propose a new causality learning framework powered by hypergraph neural networks. Extensive experiments on real-world hypergraphs verify the superiority of our framework over existing baselines.

Related papers

• Sample Efficient Bayesian Learning of Causal Graphs from Interventions [6.823521786512908]
  This study considers a Bayesian approach for learning causal graphs with limited interventional samples. We show theoretically that our proposed algorithm will return the true causal graph with high probability. We present a case study showing how this algorithm could be modified to answer more general causal questions without learning the whole graph.
  arXiv  Detail & Related papers  (2024-10-26T05:47:56Z)
• Influence Maximization in Hypergraphs Using A Genetic Algorithm with New Initialization and Evaluation Methods [6.315027378756443]
  Influence (IM) is a crucial optimization task related to analyzing complex networks in the real world. We propose a novel model that integrates the influences of both node and hyperedge failures. We then introduce genetic algorithms (GA) to identify the most influential nodes that leverage hypergraph collective influences.
  arXiv  Detail & Related papers  (2024-05-15T08:46:33Z)
• How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers? [49.35105290167996]
  Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
  arXiv  Detail & Related papers  (2023-08-17T20:40:30Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation [60.50994154879244]
  Recent studies show that Graph Neural Networks are vulnerable and easily fooled by small perturbations. In this work, we focus on the emerging but critical attack, namely, Graph Injection Attack. We propose a general defense framework CHAGNN against GIA through cooperative homophilous augmentation of graph data and model.
  arXiv  Detail & Related papers  (2022-11-15T11:44:31Z)
• Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders [19.32843499761667]
  We aim to learn the effect of a single-intervention from both observational data and sets of interventions. We provide an algorithm that learns the causal model parameters by pooling data from different regimes. The effectiveness of our method is empirically demonstrated on both synthetic and real-world data.
  arXiv  Detail & Related papers  (2022-10-11T13:42:36Z)
• DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction [53.76601630407521]
  We propose a hypergraph network model called DisenHCN to bridge the gaps in existing solutions. In particular, we first unify fine-grained user similarity and the complex matching between user preferences andtemporal activity into a heterogeneous hypergraph. We then disentangle the user representations into different aspects (location-aware, time-aware, and activity-aware) and aggregate corresponding aspect's features on the constructed hypergraph.
  arXiv  Detail & Related papers  (2022-08-14T06:51:54Z)
• Active Bayesian Causal Inference [72.70593653185078]
  We propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning. ABCI jointly infers a posterior over causal models and queries of interest. We show that our approach is more data-efficient than several baselines that only focus on learning the full causal graph.
  arXiv  Detail & Related papers  (2022-06-04T22:38:57Z)
• 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)

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.