Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs

• URL: http://arxiv.org/abs/2411.07006v1
• Date: Mon, 11 Nov 2024 14:05:39 GMT
• Title: Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs
• Authors: Malte Luttermann, Tanya Braun, Ralf Möller, Marcel Gehrke,
• Abstract summary: We show how lifting can be applied to causal inference in partially directed graphs. We show how causal inference can be performed on a lifted level in partially directed causal factor graphs.
• Score: 4.647149336191891
• License:
• Abstract: Lifting uses a representative of indistinguishable individuals to exploit symmetries in probabilistic relational models, denoted as parametric factor graphs, to speed up inference while maintaining exact answers. In this paper, we show how lifting can be applied to causal inference in partially directed graphs, i.e., graphs that contain both directed and undirected edges to represent causal relationships between random variables. We present partially directed parametric causal factor graphs (PPCFGs) as a generalisation of previously introduced parametric causal factor graphs, which require a fully directed graph. We further show how causal inference can be performed on a lifted level in PPCFGs, thereby extending the applicability of lifted causal inference to a broader range of models requiring less prior knowledge about causal relationships.

Related papers

• Lifted Causal Inference in Relational Domains [5.170468311431656]
  We show how lifting can be applied to efficiently compute causal effects in relational domains. We present the lifted causal inference algorithm to compute causal effects on a lifted level.
  arXiv  Detail & Related papers  (2024-03-15T10:44:27Z)
• Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies [58.179981892921056]
  This work introduces a novel principle for disentanglement we call mechanism sparsity regularization. We propose a representation learning method that induces disentanglement by simultaneously learning the latent factors. We show that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
  arXiv  Detail & Related papers  (2024-01-10T02:38:21Z)
• Estimation of a Causal Directed Acyclic Graph Process using Non-Gaussianity [3.04585143845864]
  We propose a new approach to discover causal dependencies in machine learning and data mining. The CGP-LiNGAM has significantly fewer model parameters and deals with only one causal graph for interpreting the causal relations.
  arXiv  Detail & Related papers  (2022-11-24T21:09:55Z)
• CLEAR: Generative Counterfactual Explanations on Graphs [60.30009215290265]
  We study the problem of counterfactual explanation generation on graphs. A few studies have explored counterfactual explanations on graphs, but many challenges of this problem are still not well-addressed. We propose a novel framework CLEAR which aims to generate counterfactual explanations on graphs for graph-level prediction models.
  arXiv  Detail & Related papers  (2022-10-16T04:35:32Z)
• OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks [42.539085765796976]
  This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for graph neural networks (GNNs) We construct a distinct generative model and design an objective function that encourages the generative model to produce causal, compact, and faithful explanations. We show that OrphicX can effectively identify the causal semantics for generating causal explanations, significantly outperforming its alternatives.
  arXiv  Detail & Related papers  (2022-03-29T03:08:33Z)
• Effect Identification in Cluster Causal Diagrams [51.42809552422494]
  We introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs) C-DAGs allow for the partial specification of relationships among variables based on limited prior knowledge. We develop the foundations and machinery for valid causal inferences over C-DAGs.
  arXiv  Detail & Related papers  (2022-02-22T21:27:31Z)
• Causal Inference Using Tractable Circuits [11.358487655918676]
  We show that probabilistic inference in the presence of unknown causal mechanisms can be tractable for models that have traditionally been viewed as intractable. This has been enabled by a new technique that can exploit causal mechanisms computationally but without needing to know their identities. Our goal is to provide a causality-oriented exposure to these new results and to speculate on how they may potentially contribute to more scalable and versatile causal inference.
  arXiv  Detail & Related papers  (2022-02-07T00:09:39Z)
• 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)
• Fuzzy Stochastic Timed Petri Nets for Causal properties representation [68.8204255655161]
  Causal relations are frequently represented by directed graphs, with nodes denoting causes and links denoting causal influence. Common methods used for graphically representing causal scenarios are neurons, truth tables, causal Bayesian networks, cognitive maps and Petri Nets. We will show that, even though the traditional models are able to represent separately some of the properties aforementioned, they fail trying to illustrate indistinctly all of them.
  arXiv  Detail & Related papers  (2020-11-24T13:22:34Z)
• Causal Expectation-Maximisation [70.45873402967297]
  We show that causal inference is NP-hard even in models characterised by polytree-shaped graphs. We introduce the causal EM algorithm to reconstruct the uncertainty about the latent variables from data about categorical manifest variables. We argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
  arXiv  Detail & Related papers  (2020-11-04T10:25:13Z)

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.