New method estimates treatment effects in network data, accounting for spillover effects.
problem Treatment effect estimation in networks with spillover effects.
method Augmented inverse probability weighting (AIPW) with cross-fitting and machine learning.
result Semiparametric treatment effect estimator converges at parametric rate and follows Gaussian distribution.
Method estimates heterogeneous causal effects on networks using orthogonal learning.
problem Challenges in estimating causal effects on networks due to treatment effects on both treated and neighbors, and network homophily.
method Two-stage orthogonal learning framework: first stage uses graph neural networks for nuisance components, second stage residualizes and interpretable attention-based model for causal effects.
result Improves heterogeneous effect estimation and supports interpretable analyses.
Integrates nearest neighbors with neural networks for more accurate treatment effect estimation.
problem Inaccurate causal effect estimations from observational data.
method NNCI methodology integrating nearest neighbors with neural network models.
result Improves treatment effect estimations on various benchmarks.
Graph neural networks help assess how global changes affect plant-pollinator networks.
problem Interpreting GNN results to understand how global changes impact plant-pollinator networks.
method Simulation study and application on Spipoll dataset to assess effects of global changes on pollination networks.
result GNNs can detect interactive effects between covariates and plant genera on pollination network connectivity.
Estimates network causal effects considering contagion and latent confounding.
problem Determining if correlations in network studies are due to contagion or latent confounding.
method Segregated graph representation, likelihood ratio tests, network causal effect estimation strategies.
result Proposes methods to estimate network causal effects under full interference scenarios.
Model clarifies network effects on CVA, revealing significant differences in derivative contract values.
problem Network effects on CVA in financial contracts.
method Developed a model to analyze default probabilities in a network of contracts.
result Network effects can significantly alter CVA values, leading to multi-modal distributions.
DREAM model improves computational efficiency for non-linear effects in relational event models.
problem Efficiently modeling non-linear effects in dynamic relational networks.
method Introduces Deep Relational Event Additive Model (DREAM) using Neural Additive Models.
result Demonstrates superior computational efficiency compared to traditional REM approaches.
MC-GMENN improves neural networks for clustered data using Monte Carlo methods.
problem Improving neural network performance on clustered data with correlations.
method MC-GMENN employs Monte Carlo methods to train generalized mixed effects neural networks.
result MC-GMENN outperforms existing models in generalization and quantifying inter-cluster variance.
This work addresses causal inference challenges in networked interference and proposes GNN-based estimators for individual treatment effects.
problem Estimating individual treatment effects in randomized experiments with networked interference.
method Uses Graph Neural Networks (GNNs) to capture network dependencies and derive causal effect estimators.
result Provides policy regret bounds and heuristic error bounds for GNN-based causal estimators under network interference and treatment capacity constraints.
Bayesian neural networks with data augmentation show a persistent cold posterior effect.
problem Understanding the cold posterior effect in Bayesian neural networks with data augmentation.
method Developed principled Bayesian neural networks using data augmentation, providing exact likelihoods and tight bounds.
result The cold posterior effect persists even in models incorporating data augmentation, suggesting it's not an artifact.
M3E2 neural network estimates multiple treatment effects.
problem Estimating effects of multiple treatments simultaneously.
method Multi-task learning neural network model for multiple treatments, continuous and binary.
result M3E2 outperforms baselines in synthetic datasets.
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.
This work discovers latent field effects governing interacting dynamical systems.
problem Discovering field effects governing interacting dynamical systems.
method Proposes neural fields to learn latent force fields from observed dynamics, disentangling local object interactions and global field effects.
result Accurately discovers latent field effects in various dynamical systems.
New framework for estimating treatment effects in experiments with network interference.
problem Network interference biases traditional treatment effect estimations in randomized experiments.
method Causal message-passing framework based on high-dimensional approximate message passing.
result Practical algorithm to estimate total treatment effect in multi-period experiments.
Bayesian method estimates causal effects with proxy networks.
problem Estimating causal effects with only proxy measurements of a latent interference network.
method Structural causal model with Block Gibbs sampler and Locally Informed Proposals.
result Accurately estimates causal effects even with noisy proxy networks.
Estimates causal effects in networks with varying interference.
problem Estimating causal effects in settings with network interference.
method Proposes neighborhood adaptive estimators for average direct treatment effect on the treated.
result Establishes rates of convergence and distributional results for proposed estimators.
Study the effects of data parallelism and sparsity on neural network training.
problem Understanding the effects of data parallelism and sparsity on neural network training.
method Conducted extensive experiments and developed a theoretical analysis.
result Found a general scaling trend between batch size and number of training steps to convergence for the effect of data parallelism, and difficulty of training under sparsity.
New neural network model improves treatment effect estimation.
problem Estimating treatment effects from observational data.
method Proposes a neural network model leveraging covariates and neighboring instances.
result Reports better treatment effect estimation performance.
This study uses complex networks to analyze influential spreaders and their effects on different market sectors.
problem Existing methods failed to distinguish between positive and negative influences of market sectors.
method LIEST (Local Influential Effects for Specific Target) method using complex network analysis.
result LIEST effectively distinguishes positive and negative influences of market sectors during different periods.
The paper presents a framework for estimating treatment effects using partial network data.
problem Estimating treatment effects when interference exists and complete network data is unavailable.
method Structural causal models and various network sampling strategies.
result Validated approach using simulated experiments and real-world applications.
CgNN uses network structure as IVs to estimate causal effects in networks.
problem Hidden confounders complicate causal effect estimation in network data.
method CgNN combines GNNs and attention mechanisms to leverage network structure as IVs.
result CgNN effectively mitigates hidden confounder bias and improves causal effect estimation.
Graph neural networks improve volatility forecasting by capturing spillover effects.
problem Forecasting multivariate realized volatility with spillover effects.
method Customized graph neural networks incorporating spillover effects from multi-hop neighbors.
result Modeling nonlinear spillover effects enhances forecasting accuracy, especially for short-term horizons.
A novel disentangled graph autoencoder improves treatment effect estimation from networked observational data.
problem Treatment effect estimation from observational data is challenging due to unconfoundedness assumption and latent confounders.
method Proposes a disentangled variational graph autoencoder to disentangle latent factors and enforce factor independence.
result Extensive experiments show superior performance compared to state-of-the-art approaches.
Simplified neural network EFTs reveal a single critical condition.
problem Understanding neuron statistics in neural networks at initialization.
method Diagrammatic approach to effective field theories (EFTs).
result A single condition governs criticality of all neuron preactivations.
Unintended effects from scaling neural network outputs with adaptive learning rates.
problem Adaptive learning rate optimization's behavior is altered by output scaling, leading to misinterpretation.
method Presented a modified optimization algorithm to mitigate unintended effects.
result Adaptive learning rate's effectiveness is significantly impacted by output scaling, especially for small scaling factors.
The paper uses neural networks to estimate treatment effects even with many confounders.
problem Estimating treatment effects with a growing number of confounders.
method General optimization framework using neural networks to approximate nuisance functions.
result Neural networks can handle a diverging number of confounders and alleviate the curse of dimensionality.
Proposes a novel neural network method to estimate average treatment effect.
problem Bias in estimating average treatment effect due to confounding and instrumental variables.
method Self-balancing neural network (Sbnet) that estimates pseudo propensity scores and average treatment effect in one step.
result Proposed method outperforms state-of-the-art methods in simulations and real-world datasets.
ResGCN detects anomalies in attributed networks by capturing sparsity and nonlinearity.
problem Detecting anomalous nodes in attributed networks.
method Attention-based deep residual modeling using Graph Convolutional Networks.
result ResGCN effectively detects anomalies in attributed networks.
A new method uses deep neural networks for estimating individual treatment effects.
problem Estimating individual treatment effects in large models.
method Extended fiducial inference with Double Neural Network (Double-NN) method.
result The Double-NN method outperforms CQR in individual treatment effect estimation.
Optimizes treatment allocation in networks considering indirect effects.
problem Finding optimal treatment allocation in network settings with interference.
method OTAPI: Optimizing Treatment Allocation in the Presence of Interference, integrating causal estimators into IM algorithms.
result OTAPI outperforms classic IM and UM approaches on synthetic and semi-synthetic datasets.
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.
The paper proposes effective margin regularization to improve adversarial robustness in deep neural networks.
problem Adversarial vulnerability of deep neural networks (DNNs).
method Regularization of effective weight norm during training to maximize effective margins.
result Effective margin regularization (EMR) boosts adversarial robustness in both standard and adversarial training.
Study efficient inference for network quantile causal effects with partial interference.
problem Estimating network causal effects on outcome quantiles with partial interference.
method Developed a nonparametric efficiency theory and a nonparametrically efficient estimator using a three-way cross-fitting procedure.
result Proposed estimator is consistent, asymptotically normal, and allows flexible estimation of nuisance functions.
We investigate robustness of deep feed-forward neural networks when input data are subject to random uncertainties. More specifically, we consider regularization of the network by its Lipschitz constant and emphasize its role. We highlight the fact that this regularization is not only a way to control the magnitude of …
This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutiona…
Neural networks learn patterns in random data, improving downstream performance.
problem Understanding what deep networks learn with random labels.
method Analytical and empirical study of convolutional and fully connected networks pre-trained on random labels.
result Pre-trained networks on random labels transfer faster to real datasets, despite specialization effects.
Neural networks can learn kernel machines with a data-dependent kernel.
problem Can neural networks in the rich feature learning regime learn a kernel machine?
method Demonstrated silent alignment effect in neural networks, showing they can learn a kernel machine with a data-dependent kernel.
result Neural networks in the rich feature learning regime can learn a kernel machine with a data-dependent kernel due to silent alignment.
Study news networks to predict stock returns.
problem Predicting cross-sectional stock returns using news networks.
method Constructed time-varying directed networks of S&P500 stocks from 1 million news articles, identified stock tickers using an algorithm, and tested for comovement and reversal effects.
result News network attention proxy, network degree, predicts monthly stock returns robustly.
Unified model for signed networks separates balance and anomaly effects.
problem Ignoring sign information in signed networks leads to inaccurate analysis.
method Low rank plus sparse matrix decomposition with regularized formulation.
result The model accurately detects communities and anomalies in signed networks.
causalKANs provides interpretable treatment effect estimates using neural networks.
problem The opacity of deep neural networks limits their adoption in sensitive domains.
method Proposes causalKANs, a framework that transforms neural estimators into interpretable closed-form formulas.
result causalKANs performs on par with neural baselines in CATE error metrics and offers a favorable accuracy--interpretability trade-off.
metabeta uses neural networks to speed up Bayesian mixed-effects regression.
problem Bayesian mixed-effects regression is computationally expensive.
method metabeta is a neural network model that pre-trains to estimate posterior distributions.
result metabeta achieves comparable performance to MCMC at a fraction of the time.
Estimates peer influence effects using embeddings for social networks.
problem Estimating peer contagion effects in social networks with unobserved confounders.
method Nonparametrically adjusts for homophily using node embeddings.
result Formalizes causal effect of peer influence in social networks.
Graph neural nets improve discrete choice modeling with network effects.
problem Modeling network effects in discrete choice problems.
method Graph Convolutional Neural Network (GCNN) architecture.
result Higher predictive performance than standard models with interpretability.
The paper proposes a neural network method to estimate treatment effects by balancing treated and control distributions.
problem Estimating individual and average treatment effects from observational data.
method Balance regularization of multi-head neural network architectures to reduce confounding effects.
result The approach reduces bias-variance trade-off and improves treatment effect estimation.
This study compares machine learning methods for high-cardinality categorical variables.
problem Machine learning struggles with high-cardinality categorical variables.
method Empirical comparison of tree-boosting, deep neural networks, and linear mixed effects models.
result Tree-boosting with random effects outperforms deep neural networks with random effects.
NIMO combines interpretability with neural networks for clear feature effects.
problem Lack of interpretability in deep learning models.
method Combines neural networks with linear regression using adaptive ridge regression.
result NIMO provides faithful and intelligible feature effects while maintaining good predictive performance.
GAMI-Net improves neural network interpretability while maintaining accuracy.
problem Lack of interpretability in neural network models.
method GAMI-Net is a disentangled feedforward network with multiple additive subnetworks designed for capturing main effects and pairwise interactions, considering sparsity, heredity, and marginal clarity.
result GAMI-Net achieves superior interpretability and competitive prediction accuracy compared to explainable boosting machine and other models.
Introduces NQ network for non-crossing quantile learning.
problem Quantile crossing issue in distributional learning.
method Non-negative activation functions ensure monotonic distributions.
result Effective for distributional reinforcement learning and causal effect estimation.