Causal Inference Using Tractable Circuits

• URL: http://arxiv.org/abs/2202.02891v1
• Date: Mon, 7 Feb 2022 00:09:39 GMT
• Title: Causal Inference Using Tractable Circuits
• Authors: Adnan Darwiche
• Abstract summary: 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.
• Score: 11.358487655918676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of this paper is to discuss a recent result which shows that probabilistic inference in the presence of (unknown) causal mechanisms can be tractable for models that have traditionally been viewed as intractable. This result was reported recently to facilitate model-based supervised learning but it can be interpreted in a causality context as follows. One can compile a non-parametric causal graph into an arithmetic circuit that supports inference in time linear in the circuit size. The circuit is also non-parametric so it can be used to estimate parameters from data and to further reason (in linear time) about the causal graph parametrized by these estimates. Moreover, the circuit size can sometimes be bounded even when the treewidth of the causal graph is not, leading to tractable inference on models that have been deemed intractable previously. This has been enabled by a new technique that can exploit causal mechanisms computationally but without needing to know their identities (the classical setup in causal inference). 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.

Related papers

• Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs [4.647149336191891]
  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.
  arXiv  Detail & Related papers  (2024-11-11T14:05:39Z)
• Estimating Causal Effects from Learned Causal Networks [56.14597641617531]
  We propose an alternative paradigm for answering causal-effect queries over discrete observable variables. We learn the causal Bayesian network and its confounding latent variables directly from the observational data. We show that this emphmodel completion learning approach can be more effective than estimand approaches.
  arXiv  Detail & Related papers  (2024-08-26T08:39:09Z)
• 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)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning [8.20217860574125]
  We propose a novel framework for learning nonlinear cyclic causal models from interventional data, called NODAGS-Flow. We show significant performance improvements with our approach compared to state-of-the-art methods with respect to structure recovery and predictive performance.
  arXiv  Detail & Related papers  (2023-01-04T23:28:18Z)
• Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA [81.4991350761909]
  Independent component analysis (ICA) refers to an ensemble of methods which formalize this goal and provide estimation procedure for practical application. We show that the latent variables can be recovered up to a permutation if one regularizes the latent mechanisms to be sparse.
  arXiv  Detail & Related papers  (2021-07-21T14:22:14Z)
• 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)
• 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)
• Structural Causal Models Are (Solvable by) Credal Networks [70.45873402967297]
  Causal inferences can be obtained by standard algorithms for the updating of credal nets. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks. Experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.
  arXiv  Detail & Related papers  (2020-08-02T11:19:36Z)
• Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations [12.47276164048813]
  We show that when transforming SPNs to a causal graph interventional reasoning reduces to computing marginal distributions. We first provide an algorithm for constructing a causal graph from a PSDD, which introduces augmented variables.
  arXiv  Detail & Related papers  (2020-01-29T15:45:47Z)

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.