Deep learning method infers causal interactions from data.
problem Causal inference from observational data.
method Transform input vectors to NEPDFs, train CNN on NEPDFs.
result Improves upon prior methods for causal inference.
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48 results for “Causal Convolutional Neural Network”
DISTANA predicts and denoises spatial wave dynamics.
problem Identifying causality in spatially distributed, non-linear dynamical processes.
method Generative, recurrent graph convolution neural network architecture (DISTANA).
result DISTANA outperforms alternative approaches in denoising and predicting complex spatial wave propagation.
New method allows real-time audio synthesis using non-causal convolutions.
problem Real-time audio synthesis limitations due to offline model constraints.
method Post-training reconfiguration of non-causal models for real-time buffer-based processing.
result Non-causal streaming models can be transformed from offline-trained models without quality loss.
Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond Recommendationcs.IR
Enhances LightGCN for credit bond recommendations with dynamic node embeddings.
problem Challenges in static embeddings for rapidly evolving user interests in finance.
method Causal graph convolution for dynamic node embeddings over chronological user-item interactions.
result Significantly enhances LightGCN performance in financial product recommendations.
Graph neural networks integrate causal knowledge for more accurate uplift modeling.
problem Identifying the most effective treatments and clients for marketing interventions.
method Combining graph neural networks with causal knowledge to estimate uplift values.
result The proposed method outperforms traditional approaches in predicting uplift values with minimal errors.
CNN improves causal inference by controlling time-structured covariates.
problem Estimating the effect of early retirement on health outcomes while controlling for time-structured covariates.
method Used CNN to fit nuisance models explaining treatment and outcome, combining them into an augmented inverse probability weighting estimator.
result Uniformly valid inference achieved through CNN, providing rates of convergence and uniformly valid inference guarantees.
CausAdv detects adversarial examples using causal reasoning.
problem Vulnerability of CNNs to adversarial perturbations.
method Causal framework based on counterfactual reasoning.
result Adversarial examples exhibit different CI distributions compared to clean samples.
New method uses neural networks to forecast spatial-temporal data.
problem Probabilistic forecasting of spatio-temporal data with causal structure.
method MMAF-guided learning with ensemble of stochastic feed-forward neural networks.
result Forecasting remains calibrated across multiple time horizons.
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probability density under both parametric and non-parametric settings. More specifically, stacked residual blocks based on dilated causal convolut…
We present a new convolutional neural network-based time-series model. Typical convolutional neural network (CNN) architectures rely on the use of max-pooling operators in between layers, which leads to reduced resolution at the top layers. Instead, in this work we consider a fully convolutional network (FCN) architect…
Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. However, efficiently modelling long-term dependencies in these sequences is still challenging. Although the receptive field of these…
IAE extracts innovations sequences for non-Gaussian processes.
problem Extracting innovations sequences for non-Gaussian processes.
method Causal convolutional neural network.
result IAE effectively detects anomalies in non-Gaussian data.
Transformers predict price movements from limit order books.
problem Predicting price movements from limit order books.
method Causal convolutional network with masked self-attention.
result Significantly outperforms existing architectures on FI-2010 dataset.
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Trainingstat.ML
We study optimal covariate balance for causal inferences from observational data when rich covariates and complex relationships necessitate flexible modeling with neural networks. Standard approaches such as propensity weighting and matching/balancing fail in such settings due to miscalibrated propensity nets and inapp…
New method uses MMAF-guided learning for spatio-temporal probabilistic forecasts.
problem Probabilistic forecasting of spatio-temporal data with causal structure.
method Generalized Bayesian methodology, MMAF-guided learning, ensemble of stochastic feed-forward neural networks.
result Forecast performance comparable to, and sometimes better than, deep learning architectures.
Graph neural networks help infer causal effects from partially observable data.
problem Inferring causal effects from partially observable data.
method Theoretical analysis of GNN and SCM connections.
result Established a new model class for GNN-based causal inference.
Blog post comparing neural network methods for causal inference.
problem Estimating heterogeneous treatment effects in causal inference.
method Developed and compared a fully connected neural network implementation of Bayesian Causal Forest.
result Improvements in performance in simulation settings.
New method quantifies intrinsic causal contributions in neural networks.
problem Measuring the causal influence of input features in deep neural networks.
method Proposes an identifiable generative post-hoc framework to quantify intrinsic causal contributions (ICC) as structural causal models.
result ICC generates more intuitive and reliable explanations compared to existing global explanation techniques.
Proposes DCNAR for dynamic causal inference from neural time series.
problem Uncertainty and evolution of causal structure in real-world domains.
method Two-stage neural causal modeling integrating discovery and inference.
result Dynamic causal inferences are more stable and meaningful than alternatives.
Causal Deep Learningcs.LG
Causal deep learning tackles causal inference using tensor factor analysis.
problem Addressing causal questions in data using neural networks.
method Tensor factor analysis and neural network architectures (causal capsules, tensor transformer, multilinear projection algorithm).
result Derives deep neural networks for causal inference with tensor factor analysis.
VC dimensions of group CNNs are infinite for certain kernels and groups.
problem Estimating the generalization capacity of group convolutional neural networks.
method Identifying precise VC dimension estimates for simple sets of group CNNs.
result Two-parameter families of convolutional neural networks have an infinite VC dimension for infinite groups and certain kernels.
Interpretable model for Granger causality using neural networks.
problem Inferring Granger causality in complex dynamical systems.
method Extension of self-explaining neural networks for multivariate Granger causality.
result Framework performs on par with baseline methods and better at inferring interaction signs.
Decomposes financial networks to reveal cause-effect hierarchies during crises.
problem Complex financial networks are hard to interpret due to Granger causality.
method Helmholtz-Hodge-Kodaira decomposition to separate networks into rotational and gradient components.
result Precious metals and pharmaceutical products are identified as causal drivers during crises.
SKI speeds up Toeplitz Neural Networks by avoiding explicit decay bias and using frequency response.
problem Efficiently compute and update Toeplitz matrices in neural networks.
method Sparse plus low-rank decomposition, asymmetric SKI, frequency response modeling.
result Achieved significant speedup with minimal performance loss.
New model captures long-range patterns in sequences efficiently.
problem Efficiently capturing long-range patterns in sequential data.
method Inspired by wavelet multiresolution analysis, introduces MultiresLayer with multiresolution convolution.
result State-of-the-art performance on sequence classification and autoregressive density estimation tasks.
Convolutional Neural Networks, as most artificial neural networks, are commonly viewed as methods different in essence from kernel-based methods. We provide a systematic translation of Convolutional Neural Networks (ConvNets) into their kernel-based counterparts, Convolutional Kernel Networks (CKNs), and demonstrate th…
Graph neural network explainer identifies causal subgraphs ensuring predictions.
problem Spurious correlations in GNN explainers.
method Proposes {
ame}, a GNN causal explainer via causal inference.
result Significantly outperforms existing GNN explainers in exact groundtruth explanation identification.
New neural network approach for optimizing latent variable models.
problem Stability issues in marginalizing Gaussian Bayesian networks.
method Developed a new graphical structure and a neural network algorithm.
result Established a duality between parameter optimization and neural network training.
A neural network finds causal relationships among latent variables.
problem Learning causal structure among latent variables in high-dimensional data.
method Redundant Input Neural Network (RINN) with modified architecture and regularized objective function.
result The RINN method successfully recovers latent causal structure between input and output variables.
CRN learns causal models using neural networks, scaling with variables and leveraging prior knowledge.
problem Challenges in learning causal models, especially scalability and leveraging prior knowledge.
method Causal Relational Networks (CRN) using continuous representations and previously learned information.
result CRN achieves high accuracy and quick adaptation to new causal models on synthetic data.
Convolutional neural networks converge quickly with gradient descent.
problem Learning efficient image classifiers with over-parameterized networks.
method Gradient descent for training over-parametrized CNNs with global average-pooling.
result Gradient descent quickly reduces the misclassification risk of CNNs.
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architecture is viewed as a Structural Causal Model, and a methodology to compute the causal effect of each feature on the output is presented. With re…
Graph Neural Network improves causal inference in dynamic systems.
problem Identifying causal relations among multi-variate time series.
method Graph Neural Network approach with score-based method.
result Graph Neural Network significantly outperformed other methods in dynamic Bayesian network inference.
Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the next element conditio…
Improved neural network convergence with causal Bayesian modeling in retail performance.
problem Improving neural network convergence in retail performance models.
method Causal Bayesian neural network implementation, removal of weakest SEM path, Flipout layers, Vadam optimizer.
result Neural network convergence improved with removal of the weakest SEM path.
We introduce causal pieces to improve spiking neural networks.
problem Improving the expressiveness and trainability of spiking neural networks.
method We decompose the input domain of SNNs into causal regions, proving that the number of these regions is a measure of SNNs' approximation capabilities.
result Parameter initialisations yielding a high number of causal pieces correlate with SNN training success.
Deep Causal Graphs model complex causal relationships using neural networks.
problem Limited applicability of parametric causal models to real-life datasets with non-linear relationships.
method Deep Causal Graphs, an abstract specification for neural networks to model causal distributions.
result Demonstrates expressive power in modelling complex interactions and provides true causal counterfactuals.
Neural causal discovery methods fail to accurately uncover causal structures due to the faithfulness property.
problem Accuracy in neural causal discovery is limited, especially when distinguishing between existing and non-existing causal relationships.
method Systematic evaluation of neural causal discovery methods, focusing on their performance in finite sample regimes and their ability to recover ground-truth graphs.
result Neural networks lack the precision to reliably recover ground-truth causal graphs, even for small graphs and large sample sizes.
Convolutional neural networks improve image classification accuracy.
problem Improving accuracy in image classification.
method Analyzing the convergence rate of misclassification risk for image classifiers.
result A rate of convergence independent of image dimension proves the effectiveness of CNNs.
CaTs use DAGs with transformers to enforce causal constraints, improving neural network robustness.
problem Neural networks lack inherent causal structure respect, leading to reliability issues.
method Introducing Causal Transformers (CaTs) that operate under predefined causal constraints specified by DAGs.
result CaTs improve robustness and interpretability of neural networks under causal constraints.
We applied pre-defined kernels also known as filters or masks developed for image processing to convolution neural network. Instead of letting neural networks find its own kernels, we used 41 different general-purpose kernels of blurring, edge detecting, sharpening, discrete cosine transformation, etc. for the first la…
Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop…
On the Universal Statistical Consistency of Expansive Hyperbolic Deep Convolutional Neural Networksstat.ML
This work proposes hyperbolic deep convolutional neural networks for better pattern recognition.
problem The limitations of Euclidean deep convolutional neural networks in capturing intricate patterns.
method Developed Hyperbolic DCNN based on Poincaré Disc, analyzing expansive convolution in non-Euclidean space.
result Hyperbolic convolutional architecture outperforms Euclidean ones in pattern recognition tasks.
Proves DCNNs with expansive convolution are strongly universally consistent.
problem Theoretical consistency of deep convolutional neural networks (DCNNs).
method Empirical risk minimization on DCNNs with expansive convolution (with zero-padding).
result DCNNs with expansive convolution are strongly universally consistent.
Proposes a method for neural networks to learn causal relationships and humans to contest and modify them.
problem Neural networks learn relevant causal relationships unclearly and are black-box, making them hard to debug.
method Two-way interaction between neural networks and humans, allowing contestation and modification of causal graphs.
result Improves predictive performance up to 2.4x and produces smaller networks up to 7x compared to SOTA.
Regularizing for or against class selectivity in DNNs improves test accuracy.
problem The necessity and sufficiency of class selectivity in DNNs.
method Direct regularization of class selectivity in convolutional neural networks.
result Reducing class selectivity improves test accuracy, while increasing it decreases it.
This research studies affine invariance in continuous-domain convolutional neural networks.
problem Recognizing patterns and features under affine transformations in continuous domains.
method Introduces a new criterion for assessing affine invariance, embeds images into the affine Lie group, and analyzes convolution over this group.
result Extends the scope of geometrical transformations that deep-learning pipelines can handle.
L-CNNs learn gauge invariant quantities on lattices.
problem Learning gauge invariant quantities on lattices.
method Novel convolutional layer preserving gauge equivariance and forming Wilson loops.
result L-CNNs can approximate any gauge covariant function on the lattice.