Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, causal inference assumptions have focused on estimation of effects for a single treatment. In this work, we construct techniques for estimation with multiple treat…
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48 results for “confounding information”
Debias recommender systems by accounting for hidden confounders using network information.
problem Debiased recommender systems to reduce bias caused by hidden confounders.
method Leverage network information to disentangle user conformity and item popularity, modeling exposure and ratings while controlling hidden confounders.
result The proposed method effectively debiases recommender systems, improving recommendation accuracy.
Study linear contextual bandits with confounded offline data, improving regret bounds.
problem Linear contextual bandits with confounded offline data.
method Construct a linear bandit algorithm that utilizes projected information.
result Proved regret bounds that improve current bounds by a factor related to visible dimensionality.
New methods prioritize acquiring confounding features for efficient treatment effect estimation.
problem Efficient treatment effect estimation from observational data with missing confounding information.
method Proposes two acquisition strategies: covariate balancing and reducing factual outcome error.
result Our proposed methods, especially reducing factual outcome error, improve sample efficiency for treatment effect estimation.
Statistical learning on biological data can be challenging due to confounding variables in sample collection and processing. Confounders can cause models to generalize poorly and result in inaccurate prediction performance metrics if models are not validated thoroughly. In this paper, we propose methods to control for …
Proposes DSW for unbiased ITE estimation with dynamic confounders.
problem Estimating ITE from dynamic observational data with time-varying confounders.
method Deep Sequential Weighting (DSW) infers hidden confounders using current treatment assignments and historical information.
result DSW generates unbiased and accurate treatment effects.
DOVI improves reinforcement learning with offline data, reducing trial-and-error in critical scenarios.
problem Lack of sample efficiency in deep reinforcement learning for critical applications.
method Proposes DOVI algorithm to incorporate confounded observational data provably efficiently.
result DOVI reduces regret by a multiplicative factor compared to pure online setting, especially when data are informative.
Counterfactual evaluation of novel treatment assignment functions (e.g., advertising algorithms and recommender systems) is one of the most crucial causal inference problems for practitioners. Traditionally, randomized controlled trials (A/B tests) are performed to evaluate treatment assignment functions. However, such…
Machine learning confound removal biases results, leading to misleading predictions.
problem Common confound removal methods in machine learning lead to misleading predictions.
method Featurewise removal of confound variance by linear regression before applying ML.
result This common deconfounding approach can leak information, amplifying null or moderate effects.
Paper tackles treatment leakage in text-based causal inference, proposing methods to mitigate bias.
problem Treatment leakage in text-as-confounder applications introduces bias in causal estimates.
method Formal definitions, four text distillation methods (passage removal, classification, salient feature removal, nullspace projection).
result Moderate distillation optimally balances bias reduction against confounder retention.
Debias concept-based explanations by removing confounding information.
problem Correlation between concepts and confounding features.
method Causal prior graph and two-stage regression technique.
result Success in removing biases and improving concept ranking.
Paper uses Shapley values to identify key confounders in product funnel data.
problem Identifying important confounders in product funnel data.
method Applies Shapley values for scalable coarsened exact matching.
result Shapley values provide robust importance-ranking for confounders.
Spectral deconfounding improves machine learning models by reducing hidden confounding effects.
problem Machine learning models can be misled by hidden confounders, leading to unreliable predictions.
method Develops a nonlinear spectral deconfounding framework for gradient boosting that modifies boosting dynamics to slow down in confounding-aligned directions.
result Spectrally deconfounded boosting improves estimation of the target function under hidden confounding and is more scalable.
CATR rationalizes text data to stabilize causal effect estimation.
problem Observational positivity violation in high-dimensional text data.
method Confounding-Aware Token Rationalization (CATR) selects necessary subset of tokens.
result CATR yields more accurate and stable causal effect estimates.
New method uses variational inference to handle confounding in imitation learning.
problem Confounding due to different sensory inputs between expert and imitating agent.
method Train variational inference model to infer expert's latent information and use for latent-conditional policy training.
result Algorithm converges to correct interventional policy and achieves asymptotically optimal performance.
Method estimates CATE using RCT data to handle hidden confounders.
problem Estimating CATE in the presence of hidden confounders.
method Pseudo-confounder generator and CATE model alignment.
result Method reduces bias in CATE estimation.
Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. In this work, we observe this limitation with respect to the task of native language identification. We find that s…
Proposes a physics-informed VAE for disentangling physics from confounding influences.
problem Challenges in inferring and predicting physical systems under partial knowledge.
method Physics-informed variational autoencoder with adversarial training.
result Model successfully disentangles known physics from confounding influences.
Over the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc) or the acquisition technology, which migh…
New method for valid prediction intervals in counterfactual outcomes with runtime confounding.
problem Valid prediction intervals for counterfactual outcomes under runtime confounding.
method Debiased machine learning framework grounded in semiparametric efficiency theory.
result Prediction intervals achieve desired coverage rates with faster convergence compared to standard methods.
Proposes methods to identify and estimate counterfactual distributions with confounding.
problem Estimating counterfactual distributions in the presence of confounding.
method Nonparametric identification and semiparametric estimation using conditional copulas and machine learning.
result Valid inference for individual-level effects and nonparametric identifiability of latent confounding subspace.
A hybrid method for causal discovery in latent confounders.
problem Estimating linear non-Gaussian models with latent confounders.
method Hybrid approach combining FCI and ICA.
result Hybrid method uniquely identifies causal relations under mild assumptions.
A novel disentangled graph autoencoder improves treatment effect estimation from networked observational data.
problem Treatment effect estimation from observational data is challenging due to unconfoundedness assumption and latent confounders.
method Proposes a disentangled variational graph autoencoder to disentangle latent factors and enforce factor independence.
result Extensive experiments show superior performance compared to state-of-the-art approaches.
Simulation study evaluates causal ML models under confounding violations.
problem Assessing conditional exchangeability in causal machine learning models.
method Simulation study with varying confounding, sample size, and NCO structures.
result Causal ML models fail to recover true treatment effect heterogeneity under violations of conditional exchangeability.
A new framework estimates policy value robustly against confounders.
problem Estimating policy value in the presence of unobserved confounders.
method Convex programming for sharp lower bounds.
result Sharp lower bounds on policy value for robust inference.
Method bounds continuous-valued treatment effects when confounding variables are hidden.
problem Inferring causal effects of continuous treatments when hidden confounders are present.
method Novel methodology to bound average and conditional average continuous-valued treatment effects.
result Method gives tighter coverage of true dose-response curve than existing methods.
Weather2vec learns representations to adjust for non-local confounding in air pollution studies.
problem Non-local confounding in evaluating environmental policies and climate events on health outcomes.
method weather2vec framework using balancing scores to learn representations of non-local information.
result The framework effectively adjusts for confounding in air pollution studies.
DFPV improves PCL for confounded bandit policy evaluation.
problem Estimating causal effects in confounded settings with high-dimensional data.
method Deep feature proxy variable method (DFPV) for high-dimensional, nonlinear relationships.
result DFPV outperforms state-of-the-art methods on synthetic benchmarks and confounded bandit problems.
Self-Distilled Disentanglement improves counterfactual predictions by separating variables.
problem Improving counterfactual predictions in the presence of confounders and unobserved variables.
method Self-Distilled Disentanglement framework based on information theory.
result Effective counterfactual inference in synthetic and real-world datasets.
Caus-Modens uses deep ensembles to better predict causal outcomes in hidden confounding scenarios.
problem Predicting causal outcomes in the presence of hidden confounders.
method Caus-Modens employs a modulated ensemble approach to improve prediction intervals for causal outcomes using sensitivity models.
result Caus-Modens provides tighter prediction intervals for causal outcomes compared to existing methods.
Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustmentsstat.AP
In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation analysis (CCA) and partial least squares (PLS). However, infor…
New method for causal discovery in high dimensions with confounder blanket assumption.
problem Inferring causal relationships from observational data in high dimensions.
method Relaxes parametric restrictions and sparsity constraints, focusing on confounder blanket.
result Provable sound and complete structure learning algorithm with finite sample error control.
MOCA uses modular attention to estimate causal effects from complex data.
problem Estimating causal effects from observational data with complex, non-linear, and high-dimensional treatment and outcome mechanisms.
method MOCA is a transformer-based framework that separates treatment and outcome modeling through modular design and one-way attention mechanism, with cutting-feedback to prevent outcome influence on treatment representations.
result MOCA outperforms classical estimators and machine learning approaches across various simulated and real-world scenarios.
Method estimates treatment effects in dyadic data with unknown confounders.
problem Estimating treatment effects in dyadic data with unobserved confounders.
method Neighborhood kernel smoothing method for graphon estimation.
result Derives rate of convergence for estimator and demonstrates test size control.
New method improves causal discovery in time series with latent confounders.
problem Low recall in causal discovery for autocorrelated time series with latent confounders.
method Iterative procedure that includes causal parents in conditioning sets, using novel orientation rules.
result Significantly higher recall compared to existing methods, especially in strong autocorrelation cases.
Framework sharpens causal effect estimates without external assumptions.
problem Estimating causal effects under unmeasured confounding.
method Information-theoretic divergence bounds, Neyman orthogonality, machine learning.
result Sharp partial identification of conditional causal effects from observational data.
Bayesian optimization improved for biased data.
problem Adversarial bias in observations, especially hidden confounders.
method Reduction to dueling bandits, information-directed sampling (IDS).
result First efficient kernelized algorithm with regret guarantees.
Proposes bounds on bias from low-dimensional representations in CATE estimation.
problem Bias in CATE estimation due to low-dimensional representations.
method Proposes a refutation framework to estimate bounds on representation-induced confounding bias.
result Demonstrates effectiveness of refutation framework in practice.
New model estimates species population trends from citizen science data.
problem Interannual confounding in citizen science data.
method Double Machine Learning framework to estimate population change and propensity scores for confounding adjustment.
result Spatially detailed trend estimates from citizen science data with low error rates.
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendationcs.LG
Reinforcement learning aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The training of the policy by reinforcement learning, however, is placed in an environment. In many real-world applications, however, the policy training in the real environmen…
We consider causal inference in the presence of unobserved confounding. We study the case where a proxy is available for the unobserved confounding in the form of a network connecting the units. For example, the link structure of a social network carries information about its members. We show how to effectively use the…
Proposes a method to improve treatment policies in data-scarce clinical settings.
problem Improving treatment policies in data-scarce clinical settings with unobserved confounding.
method Uses a causal mechanism to model the underlying generative process and augments counterfactual trajectories with source domain priors.
result Significantly improves treatment policy performance in a simulated sepsis treatment task.
Paper introduces a new measure of conditional dependence avoiding matrix inversions.
problem Measuring conditional dependence between two phenomena influenced by a confounder.
method Uses U-statistics pruning to avoid matrix inversions and re-interpret independence.
result Proposes a novel measure of conditional dependence that avoids matrix inversions.
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Informationstat.ML
Study human-machine interaction with private info using offline RL.
problem Confounding bias and distributional mismatch in offline RL for human-guided interaction.
method Developed a novel identification result and OPE method to address confounding bias, and used pessimism to tackle distributional mismatch.
result Policy pair converges to optimal one at satisfactory rate under mild assumptions.
The paper proposes a method to precisely decompose confounders and estimate treatment effects.
problem Estimating treatment effects from observational data with confounder identification and balancing.
method Learning decomposed representations to identify and balance confounders and non-confounders.
result The method achieves more precise treatment effect estimation than existing methods.
dcFCI discovers causal relationships robustly under latent confounding and mixed data.
problem Causal discovery under latent confounding and unfaithfulness.
method dcFCI integrates a new score to assess PAG compatibility, guided by FCI search.
result Significantly outperforms state-of-the-art methods in small and heterogeneous datasets.
Unified framework for causal inference with reliable uncertainty quantification.
problem Causal inference under unobserved confounding with unreliable uncertainty quantification.
method Deconditional Gaussian Process (DGP) framework for uncertainty-aware causal learning.
result Strong predictive performance and informative uncertainty quantification.
This paper tackles online strategic decision making with asymmetry and knowledge transportability.
problem Strategic decision making with information asymmetry and knowledge transportability challenges.
method Developed a sample-efficient algorithm for online learning under these conditions.
result Proved sample complexity of for learning an -optimal policy.