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.

168,657 papers · 148 categories

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48 results for “intervention decisions”

Stochastic Intervention for Causal Effect Estimationcs.AI

New method estimates stochastic intervention effects in decision-making domains.

problem Current causal inference methods are limited to deterministic treatment, unable to handle stochastic policies.

method Developed a new stochastic propensity score and stochastic intervention effect estimator (SIE) with a customized genetic algorithm (Ge-SIO).

result Empirical study shows significant performance improvement over state-of-the-art baselines.

causal inference stochastic intervention genetic algorithm propensity score decision making

2021-05-27abs ↗pdf ↗

Bayesian Intervention Optimization for Causal Discoverycs.LG

Bayesian method optimizes interventions for causal discovery.

problem Active interventions are needed for causal discovery when observational data is insufficient.

method Bayesian optimization-based approach using observational data and pre-experimental evaluation of interventions.

result Demonstrated effectiveness through various experiments.

Bayesian optimization active interventions causal discovery Bayes factors hypothesis testing

2024-06-16abs ↗pdf ↗

Causal Post-Processing of Predictive Modelsstat.ML

CPP improves predictive model outputs for better intervention decisions.

problem Predictive models often misguide resource allocation.

method Causal post-processing techniques using limited experimental data.

result CPP can improve intervention decisions, especially with imperfect causal signals.

causal post-processing predictive models intervention decisions experimental evidence resource allocation

2024-06-13abs ↗pdf ↗

Bridging Prediction and Intervention Problems in Social Systemscs.LG

This work shifts focus from prediction to intervention in social systems.

problem The limitations of focusing solely on prediction in automated decision systems.

method Shift from prediction-focused paradigm to intervention-oriented approach.

result A new perspective unifies statistical frameworks and tools for ADS design, implementation, and evaluation.

automated decision systems social systems intervention prediction statistical frameworks

2025-07-07abs ↗pdf ↗

Dynamic Causal Bayesian Optimizationstat.ML

DCBO optimizes interventions in evolving causal systems.

problem Optimal interventions in time-varying causal systems.

method Combines sequential decision making, causal inference, and GP emulation.

result DCBO identifies optimal interventions faster than competitors.

causal inference sequential decision making dynamic optimization Bayesian optimization Gaussian process

2021-10-26abs ↗pdf ↗

A scoping review of causal methods enabling predictions under hypothetical interventionsstat.ME

Review of methods enabling causal predictions under hypothetical interventions.

problem Need for predicting outcomes under hypothetical interventions in decision making.

method Systematic review of methods using causal inference for prediction models.

result Identified 13 methods for causal inference from observational data.

causal inference prediction models hypothetical interventions clinical decision making observational studies

2020-11-19abs ↗pdf ↗

The Minimal Search Space for Conditional Causal Banditscs.LG

This paper identifies the minimal set of nodes for optimal conditional interventions in causal bandits.

problem Optimizing decision-making in causal bandits with conditional interventions.

method Graphical characterization and efficient algorithm to identify the minimal set of nodes.

result The proposed algorithm significantly prunes the search space and accelerates convergence rates.

causal bandits conditional interventions graphical models multi-armed bandits optimal interventions

2025-02-10abs ↗pdf ↗

Causal Bayesian Optimizationstat.ML

Causal Bayesian Optimization improves global optimization with causal information.

problem Optimizing a system with causal relationships among variables.

method Combines causal inference, uncertainty quantification, and sequential decision making.

result Causal information significantly improves optimization strategies and reduces costs.

Bayesian Optimization Causal Inference Sequential Decision Making Optimization Real-World Applications

2020-05-24abs ↗pdf ↗

Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventionsstat.ML

Unified RL survey for healthcare AI interventions.

problem Limited real-life application of RL in healthcare.

method Unified technical survey and case studies.

result Bridge between dynamic treatment regimes and mobile health.

reinforcement learning adaptive interventions healthcare dynamic treatment regimes mobile health

2022-03-04abs ↗pdf ↗

A Statistical Test for the Benefits of Personalizing Interventionsstat.ME

Statistical test evaluates if personalizing interventions is cost-effective.

problem Balancing the benefits of personalizing interventions with their potential costs.

method Developed a statistical hypothesis test to assess the performance of personalized interventions.

result The test shows that personalized interventions can outperform standard approaches under certain conditions.

personalized interventions statistical test cost-effectiveness type-I error control asymptotic normality

2026-07-09abs ↗pdf ↗

Automatic, Debiased, and Invariant Counterfactual Generation under General Interventionsstat.ML

ADIGen: Automatic, Debiased, and Invariant Counterfactual Generation

problem Generative models for counterfactual outcomes

method ADIGen combines Riesz regression, causal invariance, and orthogonal statistical learning

result ADIGen controls counterfactual risk under general interventions

counterfactual generative models invariant debiased

2026-06-05abs ↗pdf ↗

Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventionsstat.ML

GAMBITTS uses GenAI for adaptive interventions, improving decision-making.

problem Adaptive interventions with GenAI-generated content.

method Generator-mediated bandit-Thompson sampling (GAMBITTS).

result GAMBITTS outperforms standard bandit methods in mobile health interventions.

GenAI Thompson Sampling Adaptive Interventions Mobile Health Bandit Problems

2025-05-22abs ↗pdf ↗

Decision-Making Frameworks for Network Resilience -- Managing and Mitigating Systemic (Cyber) Riskq-fin.RM

Framework for managing cyber risks in networks.

problem Managing systemic cyber risks in digital networks.

method Three components: acceptable configurations, risk mitigation interventions, and cost function.

result Effective decision-making for network resilience.

cyber risk network resilience systemic risk risk management

2023-12-21abs ↗pdf ↗

Causal Fair Machine Learning via Rank-Preserving Interventional Distributionscs.LG

New method uses causal thinking to make AI fairer decisions.

problem Designing fair machine learning models that treat equal individuals equally and unequals unequally.

method Rank-preserving interventional distributions and warping method.

result Warping method effectively identifies discriminated individuals and mitigates unfairness.

fairness machine learning causality rank-preserving interventional

2023-07-24abs ↗pdf ↗

Causal Markov Decision Processes: Learning Good Interventions Efficientlystat.ML

New formalism for decision making combines causal structures with MDPs, improving reinforcement learning performance.

problem Sequential decision making with causal knowledge to improve performance.

method Causal Markov Decision Processes (C-MDPs) and C-UCBVI algorithm exploiting causal structure.

result C-UCBVI achieves an

ilde{O}(HS\sqrt{ZT})

regret bound, independent of actions.

Causal Markov Decision Processes Reinforcement Learning Sequential Decision Making Causal Structures UCBVI

2021-02-15abs ↗pdf ↗

Towards intervention-centric causal reasoning in learning agentscs.LG

This research tackles intervention-centric causal reasoning in learning agents by using meta-learning.

problem Learning agents lack the concept of interventions, making causal learning challenging.

method A meta-reinforcement learning algorithm is used to learn causal relationships from observational data.

result The approach enables agents to learn and manipulate the environment effectively.

causal reasoning meta-learning reinforcement learning interventions learning agents

2020-05-26abs ↗pdf ↗

Algorithmic Recourse: from Counterfactual Explanations to Interventionscs.LG

As machine learning is increasingly used to inform consequential decision-making (e.g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at its decision, and also suggest actions to achieve a favorable decision. Counterfactual explanations -- "how the world would have (had) to b…

2020-02-14abs ↗pdf ↗

Quantifying Feature Contributions to Overall Disparity Using Information Theorycs.LG

New method to quantify feature contributions to disparity without access to decision-making model.

problem Quantifying feature contributions to disparity when decision-making model is not accessible.

method Use information theory to measure redundant statistical dependency between protected attribute and feature.

result Quantify feature contributions to disparity using information theory.

feature contributions disparity information theory machine learning protected attribute

2022-06-16abs ↗pdf ↗

Resolving Spurious Correlations in Causal Models of Environments via Interventionscs.LG

Causal models bring many benefits to decision-making systems (or agents) by making them interpretable, sample-efficient, and robust to changes in the input distribution. However, spurious correlations can lead to wrong causal models and predictions. We consider the problem of inferring a causal model of a reinforcement…

2020-02-12abs ↗pdf ↗

Causal Algorithmic Recourse: Foundations and Methodscs.AI

Develops a new approach for algorithmic recourse in AI systems.

problem Tackles the problem of providing recommendations for reversing negative AI decisions.

method Introduces a causal framework that models recourse as a process over pre- and post-intervention outcomes, allowing for partial stability and resampling of latent variables.

result Demonstrates the value of the proposed methods on real and semi-synthetic datasets.

algorithmic recourse causal inference latent variables post-intervention outcomes partial stability

2026-05-12abs ↗pdf ↗

The Causal Loss: Driving Correlation to Imply Causationcs.LG

Proposes Causal Loss to improve machine learning models' causal inference.

problem Machine learning algorithms often fail to capture causal relationships when data is inconsistent.

method Introduces Causal Loss, a model-agnostic loss function that enhances interventional capabilities.

result Causal Loss improves non-causal associative models to have interventional capabilities.

Causal Inference Machine Learning Interventional Correlation Causality

2021-10-22abs ↗pdf ↗

Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Healthcs.LG

Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to im…

2019-12-30abs ↗pdf ↗

Predicting Individualized Effects of Internet-Based Treatment for Genito-Pelvic Pain/Penetration Disorder: Development and Internal Validation of a Multivariable Decision Tree Modelstat.AP

Study predicts internet-based treatment effects for GPPPD based on dyadic coping.

problem Identifying which patients will benefit most from internet-based GPPPD treatment.

method Developed a multivariable decision tree model using recursive partitioning.

result Predicts large effects for high dyadic coping patients, small effects for low dyadic coping patients.

GPPPD Internet-based treatment Decision tree Recursive partitioning Coping

2023-03-15abs ↗pdf ↗

Statistical Inference in Dynamic Treatment Regimesstat.ME

Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of decision rules, with a decision rule per stage of clinical intervention; each de…

2010-06-30abs ↗pdf ↗

The Relative Value of Prediction in Algorithmic Decision Makingcs.CY

The study assesses the relative value of prediction in algorithmic decision making.

problem Improving welfare through better predictions versus other policy levers.

method Theoretical analysis of statistical models.

result Simple conditions determining the relative value of prediction.

algorithmic decision making prediction value statistical models welfare improvement

2023-12-13abs ↗pdf ↗

Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factorscs.LG

The paper proposes Tier Balancing for dynamic fairness in decision-making.

problem Achieving long-term fairness in decision-making processes.

method Causal modeling with DAGs to investigate dynamic fairness.

result Tier Balancing is a more natural approach to achieve long-term fairness, capturing latent causal factors.

fairness dynamic causal modeling long-term underlying factors

2023-01-21abs ↗pdf ↗

Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Valuescs.LG

A new decision tree method tackles fairness in datasets with missing values.

problem Fairness concerns in machine learning models trained on data with missing values.

method An integrated approach based on decision trees that incorporates missing values directly and optimizes a fairness-regularized objective function.

result Our method outperforms existing fairness intervention methods applied to imputed datasets.

fairness missing data decision trees machine learning regression

2021-09-21abs ↗pdf ↗

TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspectivecs.LG

TCFimt forecasts causal effects of multiple interventions from individual data.

problem Estimating causal effects of temporal multi-interventions from individual data.

method TCFimt uses adversarial tasks in seq2seq framework to alleviate bias and contrastive learning to decouple effects.

result TCFimt outperforms state-of-the-art methods in predicting future outcomes and choosing optimal treatments.

counterfactual forecasting temporal multi-interventions selection bias time-varying bias causal interactions

2022-12-17abs ↗pdf ↗

Model updating after interventions paradoxically introduces biasstat.ML

Updating predictive models introduces bias, leading to miscalibration.

problem Bias introduced when updating predictive models after interventions.

method Proposed a causal framework and partially observed Markov decision process.

result Successive predictive scores may converge to undesirable outcomes.

bias miscalibration updating machine learning interventions

2020-10-22abs ↗pdf ↗

Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advisingcs.CY

Study shows human advisors use context to improve student outcomes in algorithm-assisted advising.

problem How human advisors use context to guide interventions in algorithm-assisted advising.

method Mixed-methods approach combining quantitative and qualitative data from a randomized controlled trial.

result 2 out of 3 interventions by advisors were plausibly 'expertly targeted' to students using non-algorithmic context.

algorithm-assisted advising human discretion college advising student success discretionary expertise

2025-05-19abs ↗pdf ↗

Does Representation Intervention Really Identify Desired Concepts and Elicit Alignment?cs.LG

COCA refactors training data to identify and erase unsafe concepts in LLMs.

problem Identifying and erasing unsafe concepts in Large Language Models (LLMs) for safety alignment.

method Concept Concentration (COCA) refactors training data with an explicit reasoning process to identify and erase unsafe concepts.

result COCA significantly reduces both in-distribution and out-of-distribution jailbreak success rates while maintaining strong performance on regular tasks.

Representation Intervention Safety Alignment Large Language Models Concept Concentration COCA

2025-05-24abs ↗pdf ↗

DeXposure-Claw: An Agentic System for DeFi Risk Supervisioncs.AI

DeXposure-Claw supervises decentralized finance risks by grounding LLM decisions in evidence.

problem Weak evidence leads to over-interventions by general-purpose LLM agents in decentralized finance.

method DeXposure-Claw uses a graph time-series foundation model to forecast exposure networks, turning forecasts into alerts and constraining escalation with data-health gates.

result DeXposure-Claw reduces false alarms and improves regulator alignment in decentralized finance risk supervision.

decentralized finance risk supervision LLM false alarms graph time-series

2026-06-17abs ↗pdf ↗

Multi-Objective Causal Bayesian Optimizationstat.ML

MO-CBO optimizes multiple outcomes in causal systems with minimal data.

problem Optimizing multiple outcomes in causal systems with limited data.

method Decomposes MO-CBO into multi-objective optimization tasks and uses relative hypervolume improvement for sequential intervention balancing.

result MO-CBO outperforms traditional multi-objective Bayesian optimization in causal settings.

Causal Bayesian Optimization Multi-Objective Optimization Pareto Optimal Interventions Graphical Characterization Relative Hypervolume Improvement

2025-02-20abs ↗pdf ↗

Multi-task Causal Learning with Gaussian Processesstat.ML

Paper proposes a new multi-task causal Gaussian process model for better prediction and uncertainty estimation.

problem Learning causal effects of interventions on different subsets of variables in a DAG.

method DAG-GP model that allows information sharing across interventions and experiments on different variables.

result DAG-GP achieves the best fitting performance and faster optimal intervention selection compared to single-task models.

Gaussian process causal learning multi-task DAG intervention

2020-09-27abs ↗pdf ↗

FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventionscs.LG

The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact comput…

2019-11-28abs ↗pdf ↗

Learning-to-defer for sequential medical decision-making under uncertaintycs.LG

Framework for deferring decisions to experts in sequential medical settings.

problem Myopic and non-adaptive decision-making by ML models in sequential medical contexts.

method Sequential Learning-to-Defer (SLTD) framework using model-based reinforcement learning.

result Adaptive deferral policy improves trade-off between long-term outcomes and deferral frequency.

Sequential decision-making Medical decision-making Learning-to-defer Reinforcement learning Uncertainty quantification

2021-09-13abs ↗pdf ↗

Power Constrained Banditscs.LG

Develops algorithms to balance personalization and statistical power in mobile health studies.

problem Balancing personalization and statistical power in mobile health studies.

method Develops general meta-algorithms to modify existing bandit algorithms.

result Guarantees sufficient power while improving user well-being.

bandits personalization statistical power mobile health clinical trial

2020-04-13abs ↗pdf ↗

Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variablesstat.ML

Framework achieves fairness in predictions using partially known causal graph over clusters of variables.

problem Achieving fairness in algorithmic decisions when causal graph knowledge is limited.

method Leverages a causal graph over clusters of variables to train a prediction model, reducing interventional distribution discrepancies.

result Framework strikes a better balance between fairness and accuracy than existing approaches under limited causal graph knowledge.

fairness causal graph clusters of variables interventional fairness prediction model

2026-02-27abs ↗pdf ↗

Estimating Dynamic Treatment Regimes in Mobile Health Using V-learningstat.ML

The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best healthcare possible for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time an…

2016-11-10abs ↗pdf ↗

Causal Strategic Linear Regressioncs.LG

In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to "game" the decision rule by changing their features so as to receive better decisions. Whereas the strategic classification literature has previously…

2020-02-24abs ↗pdf ↗

An ASP-Based Approach to Counterfactual Explanations for Classificationcs.LG

Develops logic programs for explaining classification model decisions.

problem Creating explanations for decisions made by classification models.

method Answer-set programs for computing counterfactual interventions.

result Maximum responsibility causal explanations can be computed.

counterfactual explanations classification models answer-set programs causality-based explanations

2020-04-28abs ↗pdf ↗

BFTS: Thompson Sampling with Bayesian Additive Regression Treesstat.ML

BFTS uses Bayesian Additive Regression Trees for improved personalized mobile health interventions.

problem Adapting to complex, non-linear user behaviors in personalized mobile health interventions.

method Bayesian Forest Thompson Sampling (BFTS) integrates Bayesian Additive Regression Trees (BART) into the exploration loop of contextual bandits.

result BFTS achieves state-of-the-art regret on tabular benchmarks and improves engagement rates by over 30% in a behavioral intervention study.

Thompson Sampling Bayesian Additive Regression Trees Contextual Bandits Personalized Health Interventions Tabular Data Prediction

2026-02-08abs ↗pdf ↗

Sufficient Markov Decision Processes with Alternating Deep Neural Networksstat.ME

Advances in mobile computing technologies have made it possible to monitor and apply data-driven interventions across complex systems in real time. Markov decision processes (MDPs) are the primary model for sequential decision problems with a large or indefinite time horizon. Choosing a representation of the underlying…

2017-04-25abs ↗pdf ↗

Disaggregated Interventions to Reduce Inequalitycs.LG

Develops algorithm to reduce real-world inequality.

problem Reduces inequality in real-world disparities.

method Impact remediation framework using social science insights and constrained optimization.

result Optimal intervention policies discovered to improve equity.

algorithmic fairness inequality reduction disaggregated interventions structural causal models counterfactuals

2021-07-01abs ↗pdf ↗

RISE: Robust Individualized Decision Learning with Sensitive Variablescs.LG

RISE learns decisions with sensitive variables, improving worst-case outcomes.

problem Uncertainty and bias in decisions due to delayed sensitive variable data.

method Incorporates sensitive variables offline but not at deployment, using quantile or infimum optimization.

result Improves worst-case outcomes for individuals affected by unavailable sensitive variables.

decision learning sensitive variables robust optimization individualized decisions causal inference

2022-11-12abs ↗pdf ↗

Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregationcs.CY

The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustai…

2019-03-18abs ↗pdf ↗

DeXposure-Claw: An Agentic System for DeFi Risk Supervisioncs.AI

A system for supervising decentralized finance risks using LLMs and structured evidence.

problem Supervising decentralized finance risks

method Forecast-grounded agentic supervision system

result Developed a system that scores tickets against a regulator-aligned ground truth and false-intervention rate.

decentralized finance risk supervision LLM agentic system structured evidence

2026-06-17abs ↗pdf ↗

A Short-term Intervention for Long-term Fairness in the Labor Marketcs.GT

The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisio…

2017-11-30abs ↗pdf ↗