KRCD detects unobserved confounders in nonlinear observational data.
problem Detecting unobserved confounders in nonlinear observational studies.
method Kernel Regression Confounder Detection (KRCD) using reproducing kernel Hilbert spaces.
result KRCD outperforms existing methods and achieves superior computational efficiency.
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.
Valid causal inference with unobserved confounding in high-dimensional settings.
problem Estimating causal effects with unobserved confounders in high-dimensional data.
method Proposes methods to estimate causal effects with valid confidence intervals in the presence of unobserved confounders and high-dimensional nuisance models.
result Valid semiparametric inference can be obtained with unobserved confounding, and uncertainty intervals are proposed.
iTimER learns from reconstruction errors to represent irregularly sampled time series.
problem Learning from irregularly sampled time series with missing data.
method iTimER models reconstruction errors as a proxy for unobserved values, using a mixup strategy and a Wasserstein metric.
result iTimER outperforms state-of-the-art methods in classification, interpolation, and forecasting tasks.
GEEN uses deep learning to estimate unobserved variables from observed data.
problem Estimating unobserved variables in latent variable models.
method GEEN uses deep learning with Kullback-Leibler distance to map observed measurements to latent variable realizations.
result GEEN provides a method to identify and estimate latent variables in a class of models.
Paper adapts DML for panel data, addressing unobserved heterogeneity.
problem Estimating causal effects with panel data and unobserved heterogeneity.
method Adapting double/debiased machine learning (DML) for panel data with predictive models based on correlated random effects.
result Predictive models based on correlated random effects within DML lead to accurate coefficient estimates.
Estimates CATE under hidden confounding, accounting for bias and ignorance.
problem Learning CATE from high-dimensional data with unobserved confounders introduces bias and ignorance.
method Parametric interval estimator that accounts for hidden confounding and underrepresented samples.
result Estimator converges to tight bounds on CATE when there may be unobserved confounding.
We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with sampling of random observed and unobserved pairs, with a number of samples that grows quadratically with the corpus size, making it expensive to scale to…
Causal discovery predicts unobserved joint statistics from observed data.
problem Inferring properties of unobserved joint distributions from observed data.
method Infer causal models from observed data to predict statistical properties of unobserved sets.
result Sparse causal graphs can be more useful than dense ones in predicting unobserved joint distributions.
Estimates Granger causality with unobserved confounders using deep latent-variable recurrent neural networks.
problem Non-linear Granger causality with unobserved confounders in observational studies.
method Generative model with latent variable, variational autoencoder, recurrent neural network.
result Estimated confounders improve performance in non-linear Granger causality with multiple proxies.
Study improves sample complexity for distinguishing continuous distributions and causal relationships.
problem Distinguishing continuous distributions and causal relationships in the presence of unobserved confounding.
method Proposed an estimator of KL divergence based on von Mises expansion for closeness testing.
result Established sample complexity guarantees for causal discovery in non-linear models with continuous variables and unobserved confounding.
A new method detects unknown classes and adapts to extra dimensions in high-dimensional classification.
problem Handling unknown classes and extra variables in high-dimensional classification.
method Dimension-Adaptive Mixture Discriminant Analysis (D-AMDA) using an EM algorithm for model estimation.
result The method can adapt to unknown classes and extra dimensions in high-dimensional data.
Paper proves causal direction can be inferred from data with limited randomness.
problem Inferring causal direction from observational data with limited randomness.
method Entropy measurement and structural causal models.
result Causal direction is identifiable for most causal models with limited entropy.
Kernel methods identify treatment effects with unobserved confounding using negative controls.
problem Learning causal relationships with unmeasured confounding.
method Kernel ridge regression algorithms for nonparametric treatment effects.
result Uniform consistency and finite sample rates of convergence proved.
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 method tackles OOD robustness with a single additional variable.
problem Out-of-distribution generalization with unobserved confounders.
method Identifiability assumptions using a single additional variable.
result Superior empirical performance on benchmark tasks.
Paper identifies unobserved variables from observable data.
problem Missing variables in empirical studies.
method Function mapping from observables to unobservables based on joint distribution.
result Uniqueness of latent values in each observation.
Develops a prediction method based on sampling design.
problem Creating accurate individual predictions.
method Design-based approach using expected cross-validation results.
result Valid inference of unobserved prediction errors defined with respect to sampling design.
The paper addresses misspecification in econometric models of discrete unobserved heterogeneity.
problem Misspecification in econometric models of discrete unobserved heterogeneity.
method Generalizing previous approaches to allow multiple latent variables, developing inference results for a k-means style estimator, and proposing information criteria for model selection.
result Over-fitting can be severe in k-means style estimators when the number of clusters is over-specified.
Study causal effects on humans in mixed human-AI systems with unobserved unit types.
problem Estimating causal effects on humans in systems with unobserved unit types and interaction networks.
method Assumed human-AI prior, causal message passing (CMP) framework, subpopulation analysis.
result Consistently recover human-specific causal effects using subpopulations with varying expected human composition and treatment exposure.
Framework improves CATE estimation by aligning active learning with causal objectives.
problem High cost of outcome measurements limits CATE estimation.
method Causal-EPIG framework, targeting unobservable causal quantities.
result Strategies outperform standard baselines, revealing context-dependent optimal approaches.
WU-UCT parallelizes MCTS with linear speedup and limited performance loss.
problem Challenges in parallelizing Monte Carlo Tree Search (MCTS) due to its sequential nature.
method Introduces unobserved samples to track incomplete simulations and modify UCT tree policy.
result Achieves linear speedup and only limited performance loss with increasing parallel workers.
A neural-network model clusters subjects based on their lifetime distributions.
problem Clustering subjects into clusters based on their lifetime distributions.
method A neural-network based lifetime clustering model that maximizes divergence between empirical lifetime distributions of clusters.
result Significantly better lifetime clusters compared to competing approaches.
New method separates mixed distributions without requiring samples of each source.
problem Separating mixed distributions in machine learning and signal processing.
method Neural Egg Separation method iteratively learns to separate known from unknown distributions.
result Neural Egg Separation outperforms current methods in audio and image separation tasks.
Method learns CTMC models from steady-state data, predicting unseen states.
problem Learning CTMC models from aggregate steady-state statistics without sequence examples.
method ∞-SGD, a stochastic gradient descent method that avoids infinite sums.
result Successfully learns CTMC models and predicts unseen states.
A new method uses randomized trials to estimate the strength of unobserved confounding.
problem Unobserved confounding compromises causal conclusions from non-randomized studies.
method Designs a statistical test to detect unobserved confounding strength and estimates a lower bound.
result Estimates an asymptotically valid lower bound on unobserved confounding strength.
A matrix completion problem, which aims to recover a complete matrix from its partial observations, is one of the important problems in the machine learning field and has been studied actively. However, there is a discrepancy between the mainstream problem setting, which assumes continuous-valued observations, and some…
New method estimates treatment effects over time with unobserved confounders.
problem Estimating treatment effects from observational data with unobserved confounders.
method Sequential Deconfounder using Gaussian process latent variable model.
result Unbiased estimates of individualized treatment responses over time.
New algorithm learns optimal decisions from imperfectly observed contexts.
problem Learning optimal decisions in bandits with unobserved contexts.
method Posterior sampling algorithm for imperfectly observed contexts.
result Efficient learning from noisy imperfect observations.
DiffATD efficiently discovers targets in partially observable environments using diffusion dynamics.
problem Efficiently discovering targets in partially observable environments with limited sampling.
method DiffATD uses diffusion dynamics to maintain a belief distribution over unobserved states, balancing exploration and exploitation.
result DiffATD outperforms baselines and supervised methods in diverse domains.
Spatial first differences used to estimate effects of unobservable geographic factors on agricultural productivity.
problem Estimating causal effects in the presence of unobservable heterogeneity.
method Developed a cross-sectional research design using spatial first differences (SFD) to identify causal effects.
result New estimates for the effects of time-invariant geographic factors on long-run agricultural productivities.
New method samples from any causal effect given conditional generative models.
problem Sampling from un/conditional interventional distributions in high-dimensional data.
method Sequence of push-forward computations of conditional generative models.
result Algorithm enables sampling from any identifiable interventional distribution.
New method scores DAGs by identifying unobserved confounding.
problem Unobserved confounding complicates causal discovery.
method Score-based causal discovery algorithm that accounts for unobserved confounding.
result Sparse linear Gaussian DAGs can be recovered from observed data.
New method recovers predictions from unobservable source subpopulation in binary classification.
problem Challenging binary classification with unobservable subpopulation in source domain.
method Distribution matching method to estimate subpopulation proportions, rigorous derivation of prediction models.
result Our method outperforms naive benchmarks in synthetic and real-world datasets.
Framework tests CATE homogeneity across trials and evaluates confounding.
problem Assessing treatment effect consistency across randomized and observational studies.
method Leverages multiple randomized trials to test CATE homogeneity and compares with observational data.
result Identifies potential confounding and effect heterogeneity in treatment effects.
New method estimates policy performance under unobserved confounding.
problem Estimating policy performance when decisions depend on unobserved variables.
method Developed worst-case bounds for robust OPE under unobserved confounding.
result Efficient procedure for computing worst-case bounds, proving statistical consistency.
CDVAE estimates treatment effects over time by accounting for unobserved variables.
problem Estimating treatment effects over time in the presence of unobserved confounders.
method Causal Dynamic Variational Autoencoder (CDVAE) that addresses unconfoundedness and unobserved heterogeneity.
result CDVAE outperforms existing methods in estimating Conditional Average Treatment Effects (CATEs).
Paper tackles unobserved confounding in human-AI collaborations.
problem Unobserved confounding undermines human-AI collaboration effectiveness.
method Combines sensitivity analysis from causal inference with AI-driven statistical modeling.
result Enhances robustness and reliability of collaborative outcomes.
Impute missing events in continuous-time sequences using particle smoothing.
problem Missing events in continuous-time sequences.
method Particle smoothing with trainable bidirectional LSTM proposals.
result Imputed sequences have low Bayes risk compared to ground truth.
Proposes ρ-GNF for sensitivity analysis of unobserved confounding.
problem Sensitivity analysis of unobserved confounding in observational studies.
method Copulas and normalizing flows to estimate average causal effect (ACE) as a function of unobserved confounding strength.
result Develops ρcurve to provide bounds for ACE and identify confounding strength required to nullify ACE. Estimates individual causal effects in the presence of unobserved confounders.
problem Learning CATE under unconfoundedness violations.
method Develops a functional interval estimator that predicts bounds on individual causal effects.
result Sharp interval estimator converges to tightest bounds on CATE.
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.
Selective deconfounding improves ATE estimation with less data.
problem Estimating ATE with unobserved confounders using limited data.
method Combining confounded and deconfounded observational data for ATE estimation.
result Selective deconfounding can significantly reduce the amount of deconfounded data needed.
New method removes hidden confounders for unbiased treatment effect estimation.
problem Bias in treatment effect estimation due to unobserved confounders.
method Proposes a new debiased estimation approach via SVD to handle heterogeneous confounding.
result Established rate of convergence for the estimator under different noise conditions.
The paper tackles robust domain generalization by accounting for unobserved confounders.
problem Learning robust, generalizable models from multiple datasets in the presence of unobserved confounders.
method Defines a new invariance property for causal solutions, connects it to distributionally robust optimization, and incorporates regularization to encourage partial equality of error derivatives.
result Demonstrates the empirical effectiveness of the approach on healthcare data from various modalities.
Predicts accuracy of classifiers on unseen classes.
problem Unknown accuracy of classifiers on unseen classes.
method Defined rROC to estimate classifier accuracy on unseen classes.
result Robust algorithm CleaneX achieves better predictions.
Paper challenges recent methods for causal inference with multiple causes and unobserved confounders.
problem Causal inference with multiple causes and unobserved confounders.
method Analytical counterexamples and impossibility proofs.
result Nonparametric identification is impossible for causal inference with multiple causes and unobserved confounders.
New framework for estimating treatment effects in observational studies.
problem Estimating average treatment effects in the presence of unobserved confounders.
method Distributionally robust optimization, sensitivity models.
result Sharp bounds on average treatment effects under distributional assumptions.