This paper corrects climate model biases using a factor model approach.
problem Systematic biases in GCM outputs due to unobserved confounders.
method Factor model approach to learn latent confounders from historical data and apply them to enhance bias correction.
result Significant improvements in the accuracy of precipitation outputs.
Method uses experimental data to correct hidden confounding in observational data.
problem Hidden confounding in observational data invalidates causal conclusions.
method Experimental grounding to correct hidden confounding in causal effect models.
result Proves conditions for consistent estimator of causal effect models.
New partial models correct for confounding effects in reinforcement learning.
problem Confounding effects in partial models lead to incorrect planning.
method Introduces causally correct partial models for reinforcement learning.
result Causally correct partial models avoid confounding effects and improve planning accuracy.
Use network embeddings to correct for unobserved confounding.
problem Causal inference in the presence of unobserved confounding.
method Use network embeddings to semi-supervised predict treatments and outcomes.
result Valid causal inferences under suitable conditions on predictive model quality.
sGLMM corrects genetic associations with complex relatedness and confounding.
problem Correcting spurious associations in complex genetic data with population stratification and relatedness.
method Sparse graph-structured linear mixed model (sGLMM) that incorporates relatedness information and confounding correction.
result sGLMM outperforms existing approaches in modeling correlation from population structure and shared signals.
Estimates causal effects with selection bias and confounding using regression.
problem Estimating causal effects in presence of selection bias and confounding.
method Two-step regression estimator (TSR) that corrects for selection bias and accounts for confounding.
result TSR estimator reduces variance and is validated in simulations.
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.
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.
Proposes tests to control confounding bias in predictive models.
problem Lack of non-parametric tests for confounding bias in predictive modeling.
method Partial and full confounder tests for probing null hypotheses of unconfounded and fully confounded models.
result Reveals previously unreported or hard-to-correct confounders in machine learning models.
Graphs help identify unobserved confounders in causal inference.
problem Unobserved confounders skew causal inference from observational data.
method Causal graphical view and probabilistic factor models.
result The deconfounder algorithm makes valid inferences of intervention distributions.
New methods for handling confounding in observational studies.
problem Handling confounding variables in observational studies.
method Generalized coarsened procedures for clustering confounding variables, followed by estimation of treatment effects and variance.
result Developed a general asymptotic framework for the average causal effect estimator and variance formulae.
SDD improves DD for estimating treatment effects by adjusting for confounding.
problem Estimating treatment effects in observational studies with confounding.
method Synthesized Difference in Differences (SDD) using RCT data to infer correct slopes.
result SDD achieves state-of-the-art performance across synthetic and real datasets.
We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually a…
A new index measures confounding effects in medical data.
problem Misleading predictive performance due to unaccounted confounders.
method Introduces a novel index to quantify confounding effects.
result Validated on simulated and real-world data.
New method reduces text classification errors by learning writing style instead of content.
problem Deep neural networks learn superficial patterns specific to training data.
method Adversarial training to unlearn confounding features.
result Model generalizes better and learns writing style features.
Unified framework for large-scale hypothesis testing with confounders.
problem Bias in large-scale hypothesis testing due to unmeasured confounders.
method Unified statistical estimation and inference framework that disentangles confounding effects and jointly estimates latent and primary effects.
result Effective Type-I error control and power in hypothesis testing.
New method handles many noisy proxy controls for causal inference.
problem Causal inference with many noisy proxy controls and unknown confounders.
method Linear models with rank-restricted and sparse nuisance parameters, penalization methods.
result Estimators achieve better performance in high dimensions, especially with many proxies.
Develops a deep survival model for causal inference in longitudinal studies.
problem Estimating treatment effects on time-to-event outcomes in observational studies with time-dependent covariates.
method TCS model using potential outcomes framework and ensemble of recurrent subnetworks.
result Identifies conditional average treatment effects and individual treatment effect heterogeneity over time.
We consider to learn a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually a…
Improved method for unbiased causal discovery in presence of unobserved confounding.
problem Unbiased data synthesis for causal discovery algorithms in the presence of unobserved confounding.
method Explicit block-hierarchical ancestral sampling to address limitations of implicit parameterization.
result Our approach fully covers the space of causal models, including those generated by implicit parameterization.
New method identifies causal direction with latent confounders.
problem Identifying causal direction in presence of multiple latent variables.
method Use of joint higher-order cumulant matrix properties.
result Causal asymmetry can be seen from rank deficiency properties of cumulant matrices.
Method estimates treatment effects with continuous values, correcting for confounding.
problem Estimating treatment effects with continuous values, dealing with confounding.
method Two-stage kernel ridge regression: first stage learns response, second stage corrects for distribution shift.
result Optimal learning bounds achieved without estimating treatment density, adapts to unknown overlap and kernel spectral decay.
CONE evaluates treatment assignment functions using networked observational data to mitigate hidden confounding bias.
problem Evaluate treatment assignment functions using networked observational data with hidden confounders.
method CONE framework that learns partial representations of latent confounders and combines them for counterfactual evaluation.
result Network information mitigates hidden confounding bias in counterfactual evaluation.
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.
Algorithm recovers causal graphs in presence of latent confounders and selection bias.
problem Recovering causal graphs in the presence of latent confounders and selection bias.
method Iterative causal discovery (ICD) algorithm that relies on causal Markov and faithfulness assumptions.
result Sound and complete algorithm that recovers the equivalence class of the underlying causal graph.
DoubleGen addresses bias in generative modeling of counterfactuals.
problem Bias in generative models for counterfactual outcomes.
method Doubly robust framework that modifies generative modeling training objectives to mitigate confounding and misspecification biases.
result Successfully addresses confounding bias even if only one auxiliary model is correct.
Method identifies causal drivers from background features.
problem Distinguishing causal influence from hidden confounding.
method Stability of regression coefficients measured by statistic V.
result V converges to zero if and only if no causal drivers exist.
NICE learns a representation to avoid bad controls in causal inference.
problem Avoiding bad controls in causal inference from observational data.
method Uses invariant risk minimization (IRM) to learn a representation of covariates that avoids bad controls.
result NICE outperforms adjusting for all covariates in cases with unknown collider variables and bad controls.
New method estimates latent gene expression factors without overlap with known confounders.
problem Estimating latent variance components in gene expression data with known confounders.
method Restricted maximum-likelihood method maximizing likelihood on orthogonal subspace.
result Method reduces runtime and attains greater likelihood values than gradient-based optimizers.
New method quantifies variable importance in causal forests for treatment effect heterogeneity.
problem Lack of understanding how input variables affect treatment effect heterogeneity in causal forests.
method Developed a new importance variable algorithm for causal forests based on the drop and relearn principle.
result Shows how to handle forest retraining without a confounding variable and introduces a corrective term for confounders.
New method for robust policy evaluation in offline reinforcement learning with sequentially exogenous unobserved confounders.
problem Offline reinforcement learning in domains with unobserved confounders.
method Orthogonalized robust fitted-Q-iteration with closed-form solutions and bias-correction.
result Effective in simulations and real-world data, improving robustness and computational ease.
New algorithm identifies causal effects in latent confounding models.
problem Identifying causal effects in linear non-Gaussian models with latent confounding.
method Recursive algorithm using rank conditions on higher-order cumulants.
result Algorithm achieves comparable performance to overcomplete ICA without knowing the number of latent variables.
Approaches for testing sets of variants, such as a set of rare or common variants within a gene or pathway, for association with complex traits are important. In particular, set tests allow for aggregation of weak signal within a set, can capture interplay among variants, and reduce the burden of multiple hypothesis te…
Combines IV and observational data to estimate CATEs with low compliance and unobserved confounding.
problem Estimating CATEs in personalized medicine and analytics with observational data and weak IVs.
method Two-stage framework: first learns biased CATEs from observational data, then corrects using IV data.
result Effective in estimating CATEs with low compliance and unobserved confounding.
Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure…
New method corrects bias in missing data for matrix completion.
problem Missing data bias in matrix completion.
method Causal model and synthetic nearest neighbors (SNN) method.
result Synthetic nearest neighbors (SNN) method provides consistent and normal estimates.
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.
New method debiases counterfactual distributions using observational data.
problem Estimating counterfactual distributions under interventions without relying on observational data.
method Flow-matching approach to learn counterfactual distributions from observational data.
result Deconfounding flows outperform existing debiased counterfactual distribution estimators.
Improves prediction performance on biological data by controlling confounding factors.
problem Challenges in statistical learning due to confounding variables in biological data.
method ONION for removing confounding covariates and DANN for penalizing confounder information.
result Significant improvements in generalization performance on simulated and empirical patient data.
Develops methods for estimating effects of multiple treatments with latent confounding.
problem Estimating effects of multiple treatments in the presence of unobserved confounding.
method Two assumptions based on shared confounding and independence of treatments given the confounder. Regularization by mutual information. Tractable lower bound for treatment effects.
result Validated on simulations and clinical medicine example, demonstrating estimation of treatment effects.
Paper tackles confounding bias in estimating individual treatment effects.
problem Estimating ITE from observational data with latent confounders.
method Adversarial learning framework using noisy proxies for latent confounders.
result Unbiased estimates of ITE achieved using noisy proxies.
RAMEN corrects observational data biases for multiple environments.
problem Bias in observational data for causal inference.
method RAMEN algorithm that leverages heterogeneity of multiple data sources.
result RAMEN produces unbiased treatment effect estimates.
Research shows continual learning challenges in confounded datasets.
problem Challenges in mitigating confounders in continual learning settings.
method Formal description of continual confounders, construction of ConCon dataset.
result Standard continual learning methods fail to ignore confounders.
CRN model estimates treatment effects over time using adversarial balancing.
problem Estimating treatment effects over time in medical settings.
method Adversarial domain balancing to remove time-varying confounders.
result CRN achieves lower error in estimating counterfactuals and treatment timing.
LLMs struggle with zero-shot annotation tasks due to model-internalized priors.
problem Impact of model-internalized priors on LLM performance in zero-shot annotation tasks.
method Investigated three dimensions: familiarity, decision stickiness, and susceptibility to misaligned task definitions.
result Nearly two-thirds of zero-shot errors are resistant to correction, with a rescue rate of 34.8%. Definition-Specific Familiarity (DSF) shows a positive association with model performance.
Temporal Causal Prior-Data Fitted Networks (TCPFN) for industrial time series causal discovery
problem Estimating causal effects in industrial time series
method Temporal Causal Prior-Data Fitted Networks
result Zero-shot causal discovery with explicit reliability signals
GANs correct batch effects in biological data.
problem Batch effects confound biological data analysis.
method Generative Adversarial Networks (GANs) framework.
result GANs framework corrects batch effects without kernel selection.
Detect hidden confounding in observational data using multiple environments.
problem Detect hidden confounding in observational data.
method Theoretical framework and simulation studies to test for hidden confounding.
result The proposed procedure correctly predicts hidden confounding, especially when bias is large.