Causal blankets: Theory and algorithmic framework
- URL: http://arxiv.org/abs/2008.12568v2
- Date: Tue, 29 Sep 2020 10:11:26 GMT
- Title: Causal blankets: Theory and algorithmic framework
- Authors: Fernando E. Rosas, Pedro A.M. Mediano, Martin Biehl, Shamil Chandaria,
Daniel Polani
- Abstract summary: We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics.
Our approach is based on the notion of causal blanket, which captures sensory and active variables as dynamical sufficient statistics.
- Score: 59.43413767524033
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a novel framework to identify perception-action loops (PALOs)
directly from data based on the principles of computational mechanics. Our
approach is based on the notion of causal blanket, which captures sensory and
active variables as dynamical sufficient statistics -- i.e. as the "differences
that make a difference." Moreover, our theory provides a broadly applicable
procedure to construct PALOs that requires neither a steady-state nor Markovian
dynamics. Using our theory, we show that every bipartite stochastic process has
a causal blanket, but the extent to which this leads to an effective PALO
formulation varies depending on the integrated information of the bipartition.
Related papers
- Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm [14.980926991441345]
We show that datasets containing interventional data can be effectively extracted under realistic assumptions about the data distribution.
We introduce interventional faithfulness, which relies on comparisons between the marginal distributions of each variable across observational and interventional settings.
We also introduce Intersort, an algorithm designed to infer the causal order from datasets containing large numbers of single-variable interventions.
arXiv Detail & Related papers (2024-05-28T16:07:17Z) - Prototype-based Aleatoric Uncertainty Quantification for Cross-modal
Retrieval [139.21955930418815]
Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space.
However, the predictions are often unreliable due to the Aleatoric uncertainty, which is induced by low-quality data, e.g., corrupt images, fast-paced videos, and non-detailed texts.
We propose a novel Prototype-based Aleatoric Uncertainty Quantification (PAU) framework to provide trustworthy predictions by quantifying the uncertainty arisen from the inherent data ambiguity.
arXiv Detail & Related papers (2023-09-29T09:41:19Z) - A Semantic Approach to Decidability in Epistemic Planning (Extended
Version) [72.77805489645604]
We use a novel semantic approach to achieve decidability.
Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity.
We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
arXiv Detail & Related papers (2023-07-28T11:26:26Z) - 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) - Data Association Aware POMDP Planning with Hypothesis Pruning
Performance Guarantees [7.928094304325113]
We introduce a pruning-based approach for planning with ambiguous data associations.
Our key contribution is to derive bounds between the value function based on the complete set of hypotheses and the value function based on a pruned-subset of the hypotheses.
We demonstrate how these bounds can both be used to certify any pruning in retrospect and propose a novel approach to determine which hypotheses to prune in order to ensure a predefined limit on the loss.
arXiv Detail & Related papers (2023-03-03T18:35:01Z) - Variation-based Cause Effect Identification [5.744133015573047]
We propose a variation-based cause effect identification (VCEI) framework for causal discovery.
Our framework relies on the principle of independence of cause and mechanism (ICM) under the assumption of an existing acyclic causal link.
In the causal direction, such variations are expected to have no impact on the effect generation mechanism.
arXiv Detail & Related papers (2022-11-22T05:19:12Z) - Estimation of Bivariate Structural Causal Models by Variational Gaussian
Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
Causal mechanisms can be described by structural causal models.
One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
arXiv Detail & Related papers (2021-09-06T14:52:58Z) - Statistical learning and cross-validation for point processes [0.9281671380673306]
This paper presents the first general (parametric) statistical learning framework for point processes in general spaces.
The general idea is to carry out the fitting by predicting CV-generated validation sets using the corresponding training sets.
We numerically show that our statistical learning approach outperforms the state of the art in terms of mean (integrated) squared error.
arXiv Detail & Related papers (2021-03-01T23:47:48Z) - 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) - Latent Instrumental Variables as Priors in Causal Inference based on
Independence of Cause and Mechanism [2.28438857884398]
We study the role of latent variables such as latent instrumental variables and hidden common causes in the causal graphical structures.
We derive a novel algorithm to infer causal relationships between two variables.
arXiv Detail & Related papers (2020-07-17T08:18:19Z)
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.