We address the problem of gauging the influence exerted by a given country on the global trade market from the viewpoint of complex networks. In particular, we apply the PWP method for computing indirect influences on the world trade network.
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48 results for “indirect influence”
Develops a method to audit indirect feature influence in complex models.
problem Auditing indirect feature influence in complex, black-box models.
method Disentangled influence audits using disentangled representations.
result Can detect proxy features and show which ones affect model outcomes most.
Paper presents method to audit black-box models for indirect feature influence.
problem Understanding how features indirectly influence model predictions in black-box models.
method Develops a technique to study feature influence without knowing model internals.
result Can find attribute influences even when features are not directly used by the model.
Direct and indirect RL methods classified and compared.
problem Classifying RL methods for sequential decision making.
method Direct RL solves optimal policy directly, indirect RL solves Bellman equation.
result Direct and indirect RL methods are equivalent and can be unified.
Indirect Active Learningmath.ST
New method for learning indirectly through control variables.
problem Learning relationships when direct manipulation of variables is impossible.
method Study of indirect active learning under nonparametric models with fixed budget.
result Minimax rates for estimating relationships between variables.
Proposes a new estimator for causal mediation with continuous treatments.
problem Estimation of direct and indirect effects with continuous treatments.
method Kernel smoothing approach with cross-fitting for non-parametric estimation.
result Multiply robust and asymptotically normal estimator for continuous treatments.
Extends contagion models to include direct and indirect impacts of defaults on the environment.
problem Capturing the impact of defaults on a broader economy.
method Introduces a new model allowing direct and indirect contagion within and from a default system.
result Shows how defaults within a system can affect the environment and vice versa.
Indirect attacks can fool graph classifiers even with poisoned neighbors.
problem How to evaluate and defend graph convolutional neural networks against indirect adversarial attacks.
method Proposed a method to generate adversarial perturbations on a single node far from the target.
result 99% attack success rate within two-hops from the target in two datasets.
A model predicts influential nodes in complex networks by considering indirect interactions.
problem Identifying influential nodes in complex networks using indirect interactions.
method Proposes MOGen, a multi-order generative model that considers all indirect influences up to a maximum distance.
result MOGen consistently outperforms network models and path-based approaches in predicting influential nodes.
This study quantifies systemic risk from overlapping portfolios in the Mexican financial system.
problem Systemic risk from indirect interconnections between financial institutions.
method Represented the Mexican financial system as a bipartite network of securities and financial institutions; quantified systemic risk from overlapping portfolios.
result Total systemic risk levels underestimated by up to 50% when only direct exposures are considered.
New model identifies causal direction in nonlinear systems with observed data.
problem Identifying causal direction in nonlinear systems with observed data.
method Cascade Nonlinear Additive Noise Model (CNANM) with Variational Auto-Encoder (VAE) estimation.
result Causal direction is identifiable under suitable conditions on data generation.
We introduce a program aimed to studying problems arising from the theory of complex networks with differential geometric means. We study the propagation of influences on manifolds assuming that at each point only a finite number of propagation velocities are allowed. This leads to the computation of the volume of the …
Constructs continuous analogues for binomial and Catalan numbers.
problem No specific problem stated; focuses on mathematical construction.
method Uses techniques from convex polytopes, lattice paths, and directed manifolds.
result Develops a continuous analogue for the binomial distribution.
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.
REGOMAX analyzes EU economies' sensitivity to petroleum and gas trade from major exporters.
problem Analyzing EU economies' sensitivity to petroleum and gas trade from major exporters.
method Reduced Google matrix (REGOMAX) algorithm applied to UN COMTRADE data.
result Shows sensitivity of each EU country to petroleum and gas trade from Russia, USA, Saudi Arabia, and Norway.
New method detects key borrowers in financial networks by considering long-range interactions.
problem Identifying systemically important elements in financial networks.
method Analyzes long-range interactions, considering agent attributes and indirect impacts.
result Identifies two types of key borrowers: major players and intermediaries.
Extends causal inference to hidden mediators with proxies.
problem Identifying causal effects with hidden mediators and error-prone proxies.
method Established causal hidden mediation analysis and hidden front-door criterion.
result Identification of population intervention indirect effect possible with hidden mediators.
Study stability of trading strategy under market perturbations.
problem Dynamic stability of trading strategy under market changes.
method Established reverse conjugacy characterizations, proved continuity and convergence of indirect utility process.
result Continuity and first-order convergence of indirect utility process under market perturbations.
MediEncoder: Nonlinear Representation Learning for High-Dimensional Causal Mediation Analysisstat.ME
MediEncoder learns nonlinear representations for causal mediation analysis.
problem High-dimensional noisy covariates and mediators in biomedical studies.
method Coupled encoder-decoder architecture with cross-factor network.
result Improves estimation accuracy in high-dimensional causal mediation analysis.
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.
Business-cycles and Cash-on-Market: Pre-money Startup Valuation in the Macroeconomic Environmentq-fin.GN
Business cycles affect startup valuations, both directly and indirectly.
problem How do business cycles impact startup valuations?
method Structural Equation Model approach using a dataset of 1,089 venture capital investments.
result Business cycles impact startup valuations both directly and indirectly.
Study shows ambiguity affects optimal timing in a two-dimensional model.
problem Understanding how ambiguity influences optimal timing in a two-dimensional setting.
method Analyzes a two-dimensional optimal stopping problem with ambiguity in a multifactor model.
result Ambiguity affects the rate at which the problem is discounted, not just the growth rate of underlying processes.
Paper tackles RUL prediction with scarce data using indirect supervision.
problem Predicting RUL with indirect supervision and scarce time series data.
method Unified framework called parameterized static regression, handling data scarcity without interpolation.
result Competitive performance in prediction accuracy with simulated data scarcity.
Validity problems in clinical machine learning by indirect data labeling using consensus definitionscs.LG
Machine learning models trained on indirect data labels can fail on real-world examples.
problem Validity issues in machine learning when target labels are indirectly defined.
method Identification of problematic datasets and models using a general procedure.
result Machine learning models trained on indirect data labels will fail on real-world examples.
Unified framework for learning with indirect supervision signals.
problem Learning from indirect supervision signals when gold labels are missing or costly.
method Developed a unified theoretical framework for multi-class classification with variable supervision.
result Introduced the concept of separation to characterize learnability and generalization bounds.
Solves indirect supervision problems with linear methods.
problem Structured prediction with indirect supervision.
method Solves linear system to estimate sufficient statistics, then uses convex optimization for parameter estimation.
result Effective in learning with privacy constraints and from count-based annotations.
New method reduces indirect discrimination in insurance risk models.
problem Indirect discrimination in insurance risk models using machine learning.
method Mathematical concepts of linear algebra to reduce indirect discrimination.
result Demonstrated promising performance in a concrete case of risk selection in life insurance.
PLRM synthesizes labels from mismatched sources for better training sets.
problem Creating labeled training sets is a major challenge in machine learning.
method PLRM uses probabilistic modeling to synthesize labels from indirect supervision sources with different output spaces.
result PLRM outperforms baselines by 2%-9% on various tasks.
New framework for learning from various weak supervision types.
problem Scarcity of labeled data in real-world problems.
method Probabilistic framework based on maximum likelihood principle for deep neural networks.
result General method for learning from noisy labels, complementary labels, and coarse-grained labels.
The paper introduces a new method for interpreting model predictions by considering both direct and indirect effects.
problem Interpreting model predictions to understand the causes of decisions.
method Proposes a new approach to quantify feature relevance by combining different types of interpretations and measures.
result Integrates various types of interpretations and measures to provide meaningful insights into model predictions.
Study shows cooperation can improve everyone's market efficiency.
problem Understanding when collective cooperation improves market efficiency.
method General semimartingale framework, deriving necessary and sufficient conditions.
result Strict improvement in each agent's indirect utility when cooperation is beneficial.
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
problem Challenges in evaluating indirect effects due to unmeasured confounding and unethical exposures.
method Developed a unified identification and estimation framework using proximal causal inference.
result Unified identification and estimation of PIIE and causal effect of an intervening variable in settings with pervasive unmeasured confounding.
This study compares direct and indirect methods for estimating own funds in life insurance, finding indirect methods more effective under realistic asset-liability coupling.
problem Computing own funds for life insurers using direct and indirect methods in a risk-neutral pricing framework.
method Introduced a novel family of mixed estimators including both direct and indirect methods, integrated into a control variate framework for variance reduction.
result The indirect method is more effective under realistic asset-liability coupling, but neither method is universally superior.
New methods resolve conflicting treatment effect estimates in health tech assessments.
problem Conflicting conclusions from different sponsors analyzing the same data.
method Arbitrated indirect treatment comparisons (ArMAIC) targeting a common target population.
result Estimates treatment effects in a common target population, resolving the MAIC paradox.
Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learningecon.EM
Estimates causal effects using machine learning for binary treatment and mediator.
problem Estimating direct and indirect quantile treatment effects under selection-on-observables.
method Double/debiased machine learning estimators based on efficient score functions.
result Uniform consistency and asymptotic normality of effect estimators.
Proximal Mediation Analysis with Hidden Recanting Witnesses
problem Identifying path-specific effects in mediation analysis when recanting witnesses are unknown
method Proximal causal inference and semiparametric inference framework
result Developed three novel identification strategies and a semiparametric inference framework
Physics-informed semantic inpainting improves geostatistical modeling by incorporating indirect measurements.
problem Inferring heterogeneous geological fields from limited measurements and prior spatial statistics.
method Proposes a physics-informed semantic inpainting framework using WGAN-GP to incorporate both direct and indirect measurements.
result The method satisfies physical conservation laws and enhances inpainting performance compared to using only direct measurements.
Study compares estimators for causal mediation analysis with multiple mediators.
problem Estimating causal effects through multiple mediators in observational studies.
method Parametric and non-parametric estimators, including multiply robust and double machine learning approaches.
result Advanced estimators perform well across various settings and real data.
Network analysis shows FDI indirectly boosts trade.
problem Understanding indirect effects of FDI on trade.
method Corporate control network analysis using shortest path length and communicability.
result Corporate control positively affects trade both directly and indirectly.
Stock markets are not what we think they are: the key roles of cross-ownership and corporate treasury stockcond-mat.other
We describe and document three mechanisms by which corporations can influence or even control stock prices. (i) Parent and holding companies wield control over other publicly traded companies. (ii) Through clever management of treasury stock based on buyback programs and stock issuance, stock price fluctuations can be …
Develops exact and invariant study-based decompositions for network meta-analysis.
problem Lack of exact contribution decompositions in network meta-analysis.
method Contrast-space projection formulation of NMA, study-based definition of direct and indirect evidence.
result Exact covariance-aware decompositions of NMA estimator into direct and indirect contributions.
This paper learns prior models from indirect data efficiently.
problem Learning prior models from indirect data in Bayesian inversion.
method Generative model of prior as pushforward of Gaussian in latent space, learned by minimizing loss function.
result Efficient residual-based neural operator approximation for forward model learning.
AI detects 38% NFT trades likely manipulated, improving on indirect methods.
problem Detecting crypto wash trading using indirect methods and leaked data.
method Public NFT data analysis, direct estimation, AI-based estimator.
result AI reduces estimation errors in NFT markets, improving on indirect methods.
The study tackles indirect discrimination in insurance pricing models.
problem Indirect discrimination in insurance pricing models.
method Presented a statistical model free of proxy discrimination.
result The canonical price in the model does not satisfy group fairness axioms.
The paper addresses bias amplification in prediction and decision-making using causal analysis.
problem Bias amplification in automated systems, especially after thresholding.
method Introduces margin complement and causal decomposition of prediction disparities.
result Disparity in predictor can be decomposed into causal influences of on and .
We study the ever more integrated and ever more unbalanced trade relationships between European countries. To better capture the complexity of economic networks, we propose two global measures that assess the trade integration and the trade imbalances of the European countries. These measures are the network (or indire…
This paper tackles structure learning in indirect observations of Gaussian and non-Gaussian random vectors.
problem Learning the graphical structure of random vectors indirectly observed through a sensing matrix and corrupted noise.
method Parametric and non-parametric approaches for Gaussian and non-Gaussian distributions, respectively.
result Correct graphical structure can be recovered under indefinite sensing systems with insufficient samples.
Paper proposes an algorithm to learn DAGs with indirect dependencies.
problem Learning DAGs misses indirect dependencies in local variables.
method Two-phase algorithm using high-order HSIC for local optimization.
result OT algorithm outperforms existing methods in structure estimation.