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 “combination interventions”

Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventionsstat.ME

Synthetic Combinations learns unit-specific causal outcomes for combinatorial interventions.

problem Estimating unit-specific causal outcomes for all combinations of

p

interventions in a heterogeneous setting.

method Latent factor model with Fourier expansion sparsity, imposing structure across units and interventions.

result Synthetic Combinations provides consistent estimation with poly(r) * (N + s^2p) observations, outperforming previous methods.

causal inference combinatorial interventions factor model sparse estimation experimental design

2023-03-24abs ↗pdf ↗

Structured Learning of Compositional Sequential Interventionsstat.ML

We develop a model to predict effects of sequential interventions, clarifying their combined impact.

problem Uncertainty in generalizing behavioral predictions for combinations of interventions.

method Explicit model for composition of interventions, identifying their combined effect.

result Our compositional model aids prediction in sparse data conditions.

sequential interventions compositional modeling causal matrix factorization sparse data

2024-06-09abs ↗pdf ↗

Learning Under Adversarial and Interventional Shiftscs.LG

Combines adversarial and interventional robustness for machine learning models.

problem Designing robust models for distribution shifts in machine learning.

method RISe formulation using distributionally robust optimization.

result Demonstrates efficacy of RISe approach with synthetic and real-world datasets.

robust machine learning distribution shifts adversarial robustness interventional robustness distributionally robust optimization

2021-03-29abs ↗pdf ↗

Combining Interventional and Observational Data Using Causal Reductionsstat.ML

Method estimates causal effects from combined interventional and observational data.

problem Estimating causal effects from unobserved confounders.

method Causal reduction method replacing latent confounders with a single latent confounder.

result Improves estimation accuracy from combined data without observing all confounders.

causal inference confounding interventional data observational data latent confounders

2021-03-08abs ↗pdf ↗

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatmentscs.LG

NCoRE learns counterfactual representations for combined treatments.

problem Estimating individual response to multiple simultaneous interventions.

method Neural conditional representation with modulators for cross-treatment interactions.

result NCoRE significantly outperforms existing methods in counterfactual treatment effect estimation.

counterfactual inference neural networks treatment effects combination interventions machine learning

2021-03-20abs ↗pdf ↗

Identifying Representations for Intervention Extrapolationcs.LG

Paper tackles intervention extrapolation using identifiable representations.

problem Predicting effects of unseen interventions on outcomes.

method Combines identifiable representation learning with autoencoders to enforce linear invariance.

result Identifiable representations enable non-linear extrapolation of interventions.

representation learning intervention extrapolation causal inference autoencoders latent variables

2023-10-06abs ↗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 ↗

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 ↗

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 ↗

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 ↗

The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionsstat.ME

Bayesian method for inferring causal relations from mixed observational and interventional data.

problem Challenges in causal inference from heterogeneous study conditions with unknown soft interventions.

method Interventional Bayesian Gaussian equivalent score for a mixture of observational and interventional data.

result Effective characterisation of posterior distributions of causal structures and intervention effects.

Bayesian causal inference soft interventions mixed data posterior distributions experimental studies

2022-05-05abs ↗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 ↗

Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setsstat.ML

Scientific practice typically involves repeatedly studying a system, each time trying to unravel a different perspective. In each study, the scientist may take measurements under different experimental conditions (interventions, manipulations, perturbations) and measure different sets of quantities (variables). The res…

2014-03-10abs ↗pdf ↗

Intervention Efficient Algorithms for Approximate Learning of Causal Graphscs.DS

Efficient algorithms learn causal graphs with minimal interventions.

problem Learning causal relationships between observed variables in the presence of latents.

method Bi-criteria approximation goal combining intervention design and graph property testing.

result Achieve intervention cost within a small constant factor of the optimal.

causal inference graph learning intervention design graph property testing approximation algorithms

2020-12-27abs ↗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 ↗

Identifiability Guarantees for Causal Disentanglement from Soft Interventionsstat.ML

Paper shows identifiability of causal models with unobserved variables.

problem Identify latent variables in causal models with unobserved variables.

method Developed an autoencoding variational Bayes algorithm.

result Identifiability achieved with generalized faithfulness assumptions.

causal disentanglement latent variables interventional data autoencoding variational Bayes

2023-07-12abs ↗pdf ↗

Observational and Interventional Causal Learning for Regret-Minimizing Controlstat.ML

Extended LPCMCI learns causal models from interventional data to minimize prediction error.

problem Optimizing prediction of target variables using causal models.

method Combining observational and interventional causal discovery methods.

result Extended LPCMCI allows 60.9% optimal prediction of target variables compared to 53.6% with original LPCMCI.

causal discovery regret minimization interventional data prediction error time series analysis

2022-12-05abs ↗pdf ↗

Causal Bandits: Learning Good Interventions via Causal Inferencestat.ML

We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm t…

2016-06-10abs ↗pdf ↗

Bandit Data-Driven Optimizationcs.LG

A new framework for optimizing interventions with limited data.

problem Small data, default intervention data, unmodeled objectives, unforeseen consequences.

method Bandit data-driven optimization combining online bandit learning and offline predictive analytics.

result PROOF algorithm achieves no-regret and superior performance in simulations and real-world application.

bandit learning data-driven optimization machine learning non-profit public sector

2020-08-26abs ↗pdf ↗

Learning Neural Causal Models from Unknown Interventionsstat.ML

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theoretical limitations on the identifiability of underlying structures obtained from observational data alone. Interventional data provides muc…

2019-10-02abs ↗pdf ↗

BayesIMP: Uncertainty Quantification for Causal Data Fusionstat.ML

BayesIMP combines multiple causal graphs to estimate average treatment effects with uncertainty.

problem Uncertainty quantification in causal inference from multiple datasets.

method Bayesian Interventional Mean Processes (BayesIMP) integrating probabilistic integration and kernel mean embeddings.

result Improvements in average treatment effect estimation over state-of-the-art methods.

Bayesian Causal inference Data fusion Uncertainty quantification Reproducing kernel Hilbert space

2021-06-07abs ↗pdf ↗

Probabilistic Factorial Experimental Design for Combinatorial Interventionscs.LG

A new experimental design method for combinatorial interventions reduces complexity and improves accuracy.

problem Efficiently conducting all possible combinatorial interventions with multiple treatments and potential interactions.

method Probabilistic factorial experimental design, applying random combinations of treatments and adapting over multiple rounds.

result Optimal dosage of 1/2 for each treatment yields near-optimal design for estimating any k-way interaction model.

experimental design combinatorial interventions active learning factorial design active adaptation

2025-06-03abs ↗pdf ↗

Unifying Gaussian LWF and AMP Chain Graphs to Model Interferencestat.ML

An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can re…

2018-11-11abs ↗pdf ↗

Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Modelsstat.ML

Optimizes experiment design for causal structure learning in linear models with cycles.

problem Causal structure learning from combined observational and interventional data in linear non-Gaussian cyclic models.

method Combinatorial characterization of equivalence classes, adaptive stochastic optimization, greedy policy with near-optimal performance guarantee, sampling-based estimator for reward function.

result Optimal experiment design reduces the equivalence class of causal graphs to a single true graph with a small number of interventions.

causal inference experiment design linear models non-Gaussian cyclic models

2025-09-25abs ↗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 ↗

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 ↗

Stable Blanket with Hidden Variables and Cyclesstat.ML

This paper extends stable blanket theory to models with hidden variables and causal cycles.

problem Identifying stable predictors in models with hidden variables and causal cycles.

method Use acyclic directed mixed graphs (ADMGs) and directed graphs (DGs) with

m

-separation and

σ

-separation to characterize and construct intervention-stable predictor sets.

result Graphical characterizations of Markov blankets, stable frontiers, and stable blankets in models with hidden variables and cycles.

stable blanket hidden variables causal cycles intervention-stable predictors graphical models

2026-05-03abs ↗pdf ↗

Causal Markov Boundariesstat.ML

Paper improves feature selection for predicting outcomes from observational data.

problem Feature selection for post-intervention outcome prediction from pre-intervention variables in healthcare settings.

method Extends Markov boundary concept to treatment-outcome pairs, uses observational and experimental data.

result Combining observational and experimental data improves feature selection and effect estimation.

feature selection post-intervention outcome prediction observational data Markov boundary treatment effect

2021-03-12abs ↗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 ↗

Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systemscs.LG

This research integrates human interaction into reinforcement learning to improve sample efficiency and real-time learning.

problem Current reinforcement learning requires thousands of samples to converge, and is prone to catastrophic failures.

method Integrates human interaction modalities (demonstrations, interventions, evaluations) into the reinforcement learning loop.

result Human interaction accelerates learning and improves sample efficiency.

reinforcement learning human interaction sample efficiency real-time learning cycle-of-learning

2020-08-30abs ↗pdf ↗

Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Modelstat.ML

New model analyzes customer churn with tensor completion and binary data.

problem Analyzing the impact of interventions on customer churn.

method Tensorized latent factor block hazard model with 1-bit tensor completion.

result Effective categorization of interventions by similar impacts.

customer churn causal analysis tensor completion 1-bit tensor interventions

2024-05-18abs ↗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 ↗

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 ↗

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 ↗

Cyclic Causal Discovery from Continuous Equilibrium Datacs.LG

We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data. Novel aspects of the proposed method are its ability to work with continuous data (without assuming linearity) and to deal with feedback loops. Within the context of biochemical reactions, we a…

2013-09-26abs ↗pdf ↗

Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventionsstat.ML

New monitoring method detects ML risk models' performance changes in medical interventions.

problem Monitoring ML risk models in healthcare is complicated by confounding medical interventions.

method Developed a new score-based CUSUM monitoring procedure with dynamic control limits.

result Valid inference is possible if conditional exchangeability or time-constant selection bias hold.

machine learning risk prediction confounding interventions monitoring cumulative sum

2022-11-17abs ↗pdf ↗

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 ↗

SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trialscs.LG

We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices. The learning algorithm has a low computational complexity, preferably within few minutes using a laptop. In addition the algorithm has a l…

2019-05-17abs ↗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 ↗

Adapting Fairness Interventions to Missing Valuescs.LG

New algorithms handle missing data to improve fairness in machine learning.

problem Missing values in data can lead to unfair outcomes in machine learning models.

method Developed scalable and adaptive algorithms to handle missing values while preserving predictive information.

result Our adaptive algorithms consistently achieve higher fairness and accuracy than standard impute-then-classify methods.

missing data algorithmic fairness machine learning classification adaptive algorithms

2023-05-30abs ↗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 ↗

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 ↗

Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health studystat.ML

New method improves mHealth user engagement using Thompson sampling for count data.

problem Optimizing mHealth interventions for distal outcomes through proximal context.

method Combines count data models with Thompson sampling for contextual bandits.

result Improves user engagement in mHealth trials compared to existing methods.

mHealth Thompson sampling contextual bandits count data user engagement

2023-11-24abs ↗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 ↗

On a Reinforcement Learning Methodology for Epidemic Control, with application to COVID-19stat.ME

Paper uses RL to optimize ICU load during COVID-19.

problem Optimizing ICU load during a pandemic.

method Combines epidemic model, Bayesian inference, and RL for adaptive intervention levels.

result RL policies reduce ICU burden compared to historical interventions.

Reinforcement Learning Epidemic Control Bayesian Inference COVID-19 Intensive Care Units

2025-11-22abs ↗pdf ↗