On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

48 results for “noise interventions”

Learning Mixtures of Unknown Causal Interventionsstat.ML

New method disentangles mixed interventional and observational data in SEMs.

problem Learning causal relationships from mixed interventional and observational data.

method Developed a method to disentangle mixed interventional and observational data in linear SEMs with Gaussian noise.

result The method can identify causal graphs up to their interventional Markov Equivalence Class.

Causal Discovery Interventional Data Linear SEMs Gaussian Noise Markov Equivalence

2024-10-31abs ↗pdf ↗

Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Datacs.LG

Method identifies unknown intervention targets in structural causal models from diverse data.

problem Identifying unknown intervention targets in structural causal models from heterogeneous data.

method Two-phase approach: first recovers exogenous noises, second matches with endogenous variables.

result Proposed method uniquely identifies intervention targets under causal sufficiency assumption.

structural causal models heterogeneous data intervention targets exogenous noises endogenous variables

2023-12-11abs ↗pdf ↗

Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confoundersstat.ML

Method learns causal effects from multiple interventions in presence of unobserved confounders.

problem Disentangling causal effects from sets of interventions in the presence of unobserved confounders.

method Non-linear structural causal models with additive, multivariate Gaussian noise; algorithm that learns causal model parameters by pooling data from different regimes and maximizing combined likelihood.

result Identification proofs demonstrate that causal effects of single interventions can be learned from sets of interventions, even with unobserved confounders.

Causal inference Multiple interventions Unobserved confounders Non-linear models Gaussian noise

2022-10-11abs ↗pdf ↗

Causality-oriented robustness: exploiting general noise interventionsstat.ME

Proposes DRIG for robust predictions using noise interventions.

problem Developing robust prediction models against distribution shifts.

method Distributional Robustness via Invariant Gradients (DRIG) exploiting general noise interventions.

result DRIG yields robust predictions among a data-dependent class of distribution shifts.

causality robustness distributional robustness noise interventions distribution shifts

2023-07-18abs ↗pdf ↗

Identification of Average Causal Effects in Confounded Additive Noise Modelsstat.ME

Paper tackles confounded ANMs, estimating ACEs with minimal interventions.

problem Estimating causal effects in the presence of unobserved confounders.

method Interventional distributions and randomized algorithm to reduce the number of required interventions.

result Poly-logarithmic number of interventions sufficient to infer causal effects in confounded ANMs.

confounded ANMs average causal effects interventional distributions poly-logarithmic interventions

2024-07-13abs ↗pdf ↗

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionsstat.ME

GnIES recovers causal structure from unknown interventions.

problem Recovering causal structure from unknown interventions.

method Characterization of equivalence classes, greedy learning algorithm GnIES.

result GnIES recovers the equivalence class of the data-generating model.

causal inference structural causal models greedy learning unknown interventions Gaussian noise

2022-11-27abs ↗pdf ↗

Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confoundersstat.ME

Estimates effects of multiple interventions with hidden confounders using single-variable interventions.

problem Estimating effects of multiple interventions in the presence of hidden confounders.

method Identifiability under nonlinear structural causal model with additive Gaussian noise; pooling and joint likelihood maximization.

result Proven identifiability and superior performance compared to baseline.

structural causal models hidden confounders joint likelihood single-variable interventions nonlinear effects

2020-05-23abs ↗pdf ↗

How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?stat.AP

Study examines robustness of NPI effectiveness models against COVID-19.

problem How do NPI effectiveness estimates vary with model assumptions and data?

method Investigated 2 NPI effectiveness models and 6 variants, evaluated robustness to unseen countries, parameters, and data.

result NPI effectiveness estimates are remarkably robust to different variables.

COVID-19 NPIs Robustness Transmission Noise Model Assumptions

2020-07-27abs ↗pdf ↗

Regularizing towards Causal Invariance: Linear Models with Proxiescs.LG

Proposes a method to create robust linear models with noisy proxies of unobserved variables.

problem Learning robust linear models to handle interventions on unobserved variables with noisy proxies.

method Regularization term that balances in-distribution performance and robustness to interventions.

result Single proxy can create prediction optimal estimators under interventions of bounded strength.

causal inference linear models robustness proxy variables interventions

2021-03-03abs ↗pdf ↗

Counterfactuals for the Futurecs.AI

New approach uses 'forward-looking' counterfactuals for treatment choice.

problem Using traditional 'retrospective' counterfactuals in treatment choice leads to counterintuitive results.

method Introduces 'counterfactual treatment choice' for forward-looking counterfactuals.

result Mismatches between interventional and forward-looking counterfactuals can lead to counterintuitive results.

counterfactuals treatment choice forward-looking retrospective exogenous noise

2022-12-07abs ↗pdf ↗

Latent Variable Models for Bayesian Causal Discoverycs.LG

A model learns causal representations from high-dimensional data.

problem Challenges in learning causal representations from high-dimensional data.

method Formulated a latent variable decoder model, Decoder BCD, for Bayesian causal discovery.

result Shows that using known intervention targets as labels helps in unsupervised Bayesian inference over structure and parameters.

Bayesian causal discovery latent variable models causal representation high dimensional data linear Gaussian additive noise

2022-07-12abs ↗pdf ↗

Quantifying intrinsic causal contributions via structure preserving interventionscs.AI

A new measure of causal influence quantifies intrinsic contributions in DAGs.

problem Quantifying intrinsic causal contributions in Directed Acyclic Graphs (DAGs).

method Recursive decomposition of node contributions, structure-preserving interventions, Shapley symmetrization.

result A measure of intrinsic causal contribution that is invariant to node relabeling.

Causal inference Directed Acyclic Graphs (DAGs)Intrinsic causal influence Structure-preserving interventions Shapley value

2020-07-01abs ↗pdf ↗

Causal Representation Learning from General Environments under Nonparametric Mixingcs.LG

New method recovers causal DAGs from general environments without strict assumptions.

problem Recovering causal DAGs from real-world data with varying distributions.

method Formalizes desiderata for causal representation learning in general environments, leveraging sufficient change conditions up to third-order derivatives.

result Fully recovers latent DAG and identifies latent variables up to minor indeterminacies under nonparametric mixing.

Causal Representation Learning General Environments Nonparametric Mixing Directed Acyclic Graphs Change Conditions

2026-04-26abs ↗pdf ↗

Causal Bandits for Linear Structural Equation Modelsstat.ML

The paper tackles causal bandits for SEMs, proposing algorithms that avoid estimating $2^N$ reward distributions.

problem Designing an optimal sequence of interventions in causal graphical models to minimize cumulative regret.

method Proposes two algorithms for causal bandits for linear structural equation models (SEMs), avoiding the estimation of

2^N

reward distributions.

result Cumulative regrets scale as

ilde{\cal O} (d^{L+\frac{1}{2}} \sqrt{NT})

under bounded noise and parameter space.

causal bandits linear structural equation models interventions cumulative regret regression

2022-08-26abs ↗pdf ↗

Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queriesstat.ML

DCM uses diffusion models to answer causal queries from observational data.

problem Answering causal queries from observational data alone.

method Diffusion models to learn causal mechanisms and generate latent encodings.

result Significant improvements over existing methods for causal query answering.

diffusion models causal inference counterfactuals latent encodings observational data

2023-02-02abs ↗pdf ↗

Generator Identification for Linear SDEs with Additive and Multiplicative Noisemath.ST

The paper identifies generators of linear SDEs with noise types.

problem Identifying the generator of linear SDEs from their solution distribution.

method Deriving sufficient and necessary conditions for additive noise, and sufficient conditions for multiplicative noise.

result Generic conditions for identifying the generator of linear SDEs with both types of noise.

linear SDEs identifiability additive noise multiplicative noise causal inference

2023-10-30abs ↗pdf ↗

Robust Contextual Bandit via the Capped-

\ell_{2}

normcs.LG

This paper considers the actor-critic contextual bandit for the mobile health (mHealth) intervention. The state-of-the-art decision-making methods in mHealth generally assume that the noise in the dynamic system follows the Gaussian distribution. Those methods use the least-square-based algorithm to estimate the expect…

2017-08-17abs ↗pdf ↗

Tensor-based second-order causal discoverystat.ML

TSCD is an algorithm for causal discovery using second-order statistics.

problem Causal discovery

method Tensor-based Second-order Causal Discovery (TSCD)

result Identifiable causal order and parameters from logarithmic number of interventions

causal discovery second-order statistics tensor-based DAG identifiability

2026-06-16abs ↗pdf ↗

Causal Autoregressive Flowsstat.ML

Causal autoregressive flows enable accurate causal inference and prediction.

problem Causal discovery and interventional predictions in machine learning.

method Autoregressive normalizing flows with fixed variable orderings.

result Causal models derived from autoregressive flows are identifiable and allow for accurate interventional and counterfactual predictions.

causal inference normalizing flows autoregressive models identifiability interventional predictions

2020-11-04abs ↗pdf ↗

Adaptive Principal Component Regression with Applications to Panel Datacs.LG

Adaptive PCR improves panel data analysis with uniform guarantees.

problem Adaptive data collection in panel data settings.

method Adapting PCR to online settings using martingale concentration.

result Time-uniform guarantees for adaptive PCR in panel data.

Principal Component Regression Panel Data Adaptive Data Collection Martingale Concentration Regret Minimization

2023-07-03abs ↗pdf ↗

Identifiability Guarantees for Causal Disentanglement from Purely Observational Datacs.LG

New method identifies latent causal factors from observational data alone.

problem Identifying latent causal factors without interventions or graphical restrictions.

method Characterization of latent factors in nonlinear causal models with additive Gaussian noise and linear mixing, using a practical algorithm based on solving a quadratic program over observed data.

result Latent causal variables can be identified up to a layer-wise transformation, and further disentanglement is not possible.

Causal disentanglement Observational data Latent factors Nonlinear models Gaussian noise

2024-10-31abs ↗pdf ↗

Learning Joint Interventional Effects from Single-Variable Interventions in Additive Modelsstat.ML

Estimates joint causal effects using single-variable interventions on nonlinear models.

problem Estimating joint causal effects from single-variable interventions.

method Identifiability result and practical estimator for decomposing causal effects.

result Joint effects can be inferred without joint interventional data for nonlinear additive models.

causal inference interventional effects additive models single-variable interventions identifiability

2025-06-05abs ↗pdf ↗

Causal Discovery with Continuous Additive Noise Modelsstat.ML

We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural equation model with an …

2013-09-26abs ↗pdf ↗

Linear Causal Representation Learning from Unknown Multi-node Interventionscs.LG

This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.

problem CRL under unknown multi-node interventions, focusing on single-node assumptions.

method Establishes identifiability results for general latent causal models under stochastic interventions.

result Identifiability up to ancestors using soft interventions, perfect identifiability using hard interventions.

Causal Representation Learning Multi-node Interventions Identifiability Linear Transformation Score Functions

2024-06-09abs ↗pdf ↗

Active Bayesian Causal Inferencecs.LG

ABCI infers causal models and queries simultaneously using Bayesian active learning.

problem Inference of causal models and effects in a two-stage process is inefficient and unnatural.

method Active Bayesian Causal Inference (ABCI) using Gaussian processes for sequentially designing experiments.

result ABCI is more data-efficient and accurate in learning causal queries from fewer samples.

Bayesian inference active learning causal discovery Gaussian processes interventional data

2022-06-04abs ↗pdf ↗

Causal Discovery under Off-Target Interventionscs.LG

This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.

problem Discovering the true causal graph from observational data with limited interventions.

method Proposes a stochastic intervention model and studies verification and search problems with approximation algorithms.

result Provides approximation algorithms with competitive ratios for verification and search problems.

causal discovery stochastic interventions Markov equivalence approximation algorithms

2024-02-13abs ↗pdf ↗

Info Interventioncs.AI

Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…

2019-07-24abs ↗pdf ↗

Learning Optimal Interventionsstat.ML

Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each individual or globally enacted over a population. For applications where harmful intervention is drastically worse than proposing no change…

2016-06-16abs ↗pdf ↗

Scalable Intervention Target Estimation in Linear Modelsstat.ME

Paper proposes scalable algorithm to estimate intervention targets in linear models.

problem Estimating intervention targets in linear models from observational and interventional data.

method The paper proposes a scalable algorithm that estimates intervention sites from the difference between precision matrices of observational and interventional datasets.

result The algorithm consistently identifies all intervention targets and updates observational Markov equivalence classes to interventional ones.

causal inference linear models interventional data precision matrices Markov equivalence

2021-11-15abs ↗pdf ↗

SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvementstat.ML

SNPL learns safe policies for multi-objective interventions with high confidence.

problem Designing effective digital interventions balancing multiple objectives with noisy data.

method Leverages algorithmic stability to learn policies with high-confidence guarantees.

result Offers dramatic improvements in safety and policy gains with smaller sample sizes.

policy learning multi-objective algorithmic stability noise reduction guardrail outcomes

2025-03-17abs ↗pdf ↗

Estimating Joint Interventional Distributions from Marginal Interventional Datastat.ME

The paper presents a method to estimate joint interventional distributions from marginal interventional data.

problem Estimating joint interventional distributions from marginal interventional data.

method The paper extends the Causal Maximum Entropy method to use interventional data and employs Lagrange duality to prove the solution lies in the exponential family.

result The method allows for causal feature selection and inference of joint interventional distributions.

Causal Maximum Entropy Interventional Data Maximum Entropy Feature Selection Joint Distribution

2024-09-03abs ↗pdf ↗

Distributional Causal Mediation via Conditional Generative Modelingstat.ML

DCMA uses generative models to analyze treatment effects on entire outcome distributions.

problem Traditional mediation analysis focuses on summary contrasts, missing complex distributional changes.

method DCMA learns conditional generative models for mediators and outcome, reconstructing interventional distributions via Monte Carlo simulation.

result DCMA captures both summary effects and rich distributional contrasts like energy distance and Wasserstein distance.

causal mediation distributional analysis generative models Monte Carlo simulation conditional modeling

2026-05-03abs ↗pdf ↗

Distributional Causal Mediation via Conditional Generative Modelingstat.ML

DCMA uses generative models to analyze complex treatment effects on outcome distributions.

problem Analyzing complex and nonlinear causal mechanisms through outcome-level summary contrasts.

method Generative learning framework for identifying and estimating treatment effects on entire outcome distributions.

result Reconstructs interventional outcome distributions via Monte Carlo forward simulation, capturing both summary and distributional contrasts.

causal mediation distributional analysis conditional generative modeling Monte Carlo simulation energy distance

2026-05-03abs ↗pdf ↗

Bayesian causal discovery from unknown general interventionsstat.ME

Bayesian method for causal discovery from unknown general interventions.

problem Learning causal DAGs from unknown interventions that modify parent sets.

method Bayesian approach with MCMC for approximating posterior DAGs and intervention targets.

result Bayesian method can identify DAGs and intervention targets up to equivalence classes.

Bayesian causal discovery general interventions Markov equivalence MCMC protein expression data

2023-12-01abs ↗pdf ↗

Interventional Causal Representation Learningstat.ML

Interventional data helps identify latent factors without distributional assumptions.

problem Identifying latent factors from interventional data without distributional assumptions.

method Leveraging geometric signatures of latent factors' support from interventional data.

result Latent causal factors can be identified up to permutation and scaling given data from perfect do-interventions.

causal representation learning interventional data latent factors geometric signatures perfect do-interventions

2022-09-24abs ↗pdf ↗

Deciphering interventional dynamical causality from non-intervention complex systemscs.LG

IntDC framework uncovers causal relationships from non-interventional data.

problem Detecting causal relationships in non-interventional complex systems.

method Interventional Embedding Entropy (IEE) for causal strength measurement.

result IEE accurately finds causal edges and quantifies causal strength robustly.

causal inference non-interventional systems delay-embedding interventional embedding entropy time series analysis

2024-06-29abs ↗pdf ↗

Quickest Causal Change Point Detection by Adaptive Interventionstat.ML

Algorithm detects causal change points quickly with adaptive interventions.

problem Detecting changes in causal models with interventions.

method Centralization technique, Kullback-Leibler divergence for intervention selection, adaptive intervention policy.

result Theoretical first-order optimality and validation through simulations and real-world studies.

change point detection causal inference intervention adaptive algorithms Kullback-Leibler divergence

2025-06-09abs ↗pdf ↗

Causal Entropy Optimizationcs.LG

Optimizes causal effects on unknown graphs using Causal Entropy Optimization.

problem Optimizing causal effects in unknown causal graphs.

method Causal Entropy Optimization (CEO) framework that generalizes Causal Bayesian Optimization (CBO). Incorporates causal structure uncertainty in surrogate models and intervention selection.

result CEO achieves faster convergence to global optimum compared to CBO and improves upon sequential structure learning.

causal inference optimization graph learning information theory Bayesian optimization

2022-08-23abs ↗pdf ↗

New metrics and search algorithms for weighted causal DAGscs.LG

New algorithms improve causal graph discovery with adaptive interventions, even under worst-case interventional costs.

problem Discover causal relationships from data with adaptive interventions and node-dependent costs.

method Define new benchmarks and provide adaptive search algorithms for causal graph discovery.

result Logarithmic approximations achieved under various settings: atomic, bounded size interventions and generalized cost objectives.

causal graph discovery adaptive interventions node-dependent costs worst-case interventional costs logarithmic approximations

2023-05-08abs ↗pdf ↗

Optimal intervention in the foreign exchange market when interventions affect market dynamicsq-fin.GN

We address the problem of optimal Central Bank intervention in the exchange rate market when interventions create feedback in the rate dynamics. In particular, we extend the work done on optimal impulse control by Cadenillas and Zapatero to incorporate temporary market reactions, of random duration and level, to Bank i…

2009-09-07abs ↗pdf ↗

Trust Your

\nabla

: Gradient-based Intervention Targeting for Causal Discoverystat.ML

GIT uses gradient estimators to target interventions for causal discovery.

problem Challenges in inferring causal structure from observational data.

method GIT uses gradient estimators to target interventions for causal discovery.

result GIT performs on par with competitive baselines, surpassing them in low-data regimes.

causal discovery gradient-based intervention targeting gradient estimator experimental design

2022-11-24abs ↗pdf ↗

Relaxing Faithfulness with Intervention-Only Causal Discoverycs.LG

Paper relaxes faithfulness assumption for causal discovery using interventions.

problem Violation of faithfulness assumption in natural systems leads to incorrect causal structure identification.

method Use intervention-immediacy faithfulness assumption to identify causal structures with hard interventions.

result Interventions contain information about causal structure that can identify causal structures when faithfulness is violated.

causal discovery intervention-immediacy faithfulness hard interventions systemic robustness faithfulness violation

2026-07-13abs ↗pdf ↗

Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifierscs.SD

Label noise is emerging as a pressing issue in sound event classification. This arises as we move towards larger datasets that are difficult to annotate manually, but it is even more severe if datasets are collected automatically from online repositories, where labels are inferred through automated heuristics applied t…

2019-10-26abs ↗pdf ↗

Modeling the Uncertainty in Electronic Health Records: a Bayesian Deep Learning Approachcs.LG

Deep learning models have exhibited superior performance in predictive tasks with the explosively increasing Electronic Health Records (EHR). However, due to the lack of transparency, behaviors of deep learning models are difficult to interpret. Without trustworthiness, deep learning models will not be able to assist i…

2019-07-14abs ↗pdf ↗

Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learningstat.ML

FMI uses matching to mimic interventions for causal feature learning.

problem Challenges in causal discovery from observational data.

method Feature Matching Intervention (FMI) using matching to emulate perfect interventions.

result FMI outperforms in identifying causal features from observational data.

Feature Matching Intervention Causal Discovery Observational Data Causal Graph Learning Out-of-Distribution Generalization

2025-03-05abs ↗pdf ↗

BaCaDI: Bayesian Causal Discovery with Unknown Interventionscs.LG

BaCaDI discovers causal structures from unknown interventions.

problem Inferring causal structures from unknown interventions with limited data.

method Bayesian framework with gradient-based variational inference.

result BaCaDI outperforms related methods in identifying causal structures and intervention targets.

causal inference Bayesian methods unknown interventions gradient-based inference single-cell data

2022-06-03abs ↗pdf ↗

Transferring Information Across Interventions in Causal Bayesian Optimizationcs.AI

Graph-coupled causal Bayesian optimization transfers information across related interventions.

problem Optimizing expensive systems where interventions are costly and causal effects are confounded.

method Ties intervention effects together through shared causal parameters, improving estimation.

result Information-gain and regret bounds show improved performance with shared mechanisms.

Bayesian optimization causal inference information transfer regret bounds graph coupling

2026-05-31abs ↗pdf ↗

Matching a Desired Causal State via Shift Interventionsstat.ME

The paper tackles matching a desired mean in causal systems through shift interventions.

problem Matching a desired mean in causal systems.

method Defining Markov equivalence classes, proposing active learning strategies, deriving lower bounds.

result Proposed active learning strategies require fewer interventions than previous approaches, especially for certain graph classes.

causal systems shift interventions mean matching active learning Markov equivalence

2021-07-05abs ↗pdf ↗