Unobserved confounding is a major hurdle for causal inference from observational data. Confounders---the variables that affect both the causes and the outcome---induce spurious non-causal correlations between the two. Wang & Blei (2018) lower this hurdle with "the blessings of multiple causes," where the correlation st…
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.
168,695 papers · 148 categories
Trend · papers per month
48 results for “Unobserved Causes”
We tackle causal discovery in linear systems with measurement error and unobserved causes.
problem Causal discovery in linear systems with measurement error and unobserved causes.
method Characterization of identifiability based on the mixing matrix, proposing causal structure learning methods.
result The structure of causal models can be identified under certain faithfulness assumptions.
Paper proposes methods to discover causal models with unobserved variables.
problem Discovering causal relationships in data with unobserved variables.
method Two methods leveraging prior knowledge for causal discovery in CAM-UV models.
result Accuracy of causal discovery improves with more prior knowledge.
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variablesstat.ML
CLOUD method detects causal relationships in various data types without latent variable assumptions.
problem Detecting causal relationships in the presence of unobserved common causes.
method CLOUD method using Normalized Maximum Likelihood (NML) Code for various data types (discrete, mixed, continuous).
result CLOUD method is more effective than existing methods in inferring causal relationships.
This work restricts hidden cardinality in causal models to infer causal relations.
problem Causal relations between variables with a common unobserved cause cannot be directly inferred.
method Derive inequality constraints from d-separation in causal models with known cardinalities of unobserved variables.
result Inference of causal relations is possible with additional assumptions about cardinalities.
Proposes -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 to provide bounds for ACE and identify confounding strength required to nullify ACE.
Unobserved confounding is a central barrier to drawing causal inferences from observational data. Several authors have recently proposed that this barrier can be overcome in the case where one attempts to infer the effects of several variables simultaneously. In this paper, we present two simple, analytical counterexam…
New method identifies causal effects with categorical unobserved confounders.
problem Estimating causal effects in the presence of unobserved confounders.
method Mixture learning and tensor decomposition for consistent estimation.
result Causal effects are identifiable with categorical unobserved confounders under suitable conditions.
We describe a method that infers whether statistical dependences between two observed variables X and Y are due to a "direct" causal link or only due to a connecting causal path that contains an unobserved variable of low complexity, e.g., a binary variable. This problem is motivated by statistical genetics. Given a ge…
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.
Causal inference from observational data often assumes "ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that…
Generative multitask learning mitigates confounders causing targets.
problem Unobserved confounders causing targets but not inputs.
method Generative multitask learning (GMTL) modifies inference objective to remove joint target influence.
result Improved robustness to target shift across multitask learning methods.
Study tackles causal structure learning in linear models with unobserved variables and measurement error.
problem Challenges of unobserved common causes and measurement error in causal structure learning.
method Introduces LV-SEM-ME model with four types of variables and characterizes identifiability under separability condition.
result Establishes form of identification robustness for target effect in broader LV-SEM-ME model.
Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose particle smoothing---a form of sequential importance sampling---to impute the missing events in an incomplete sequence. We develop a trainable fa…
Given data over variables we consider the problem of finding out whether jointly causes or whether they are all confounded by an unobserved latent variable . To do so, we take an information-theoretic approach based on Kolmogorov complexity. In a nutshell, we follow the postulate that firs…
Clarifies the theory of the deconfounder by Imai and Jiang.
problem Theoretical requirements for the deconfounder algorithm.
method Clarifies the assumption of 'no unobserved single-cause confounders' using empirical studies.
result Imai and Jiang's clarification of the assumption does not hold for counterexamples proposed by Ogburn et al. (2020).
Estimates long-term effects from short-term experiments and observational data with unobserved confounders.
problem Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding.
method Combining regression residuals with short-term experimental outcomes to create an instrumental variable for estimating long-term causal effects.
result The estimator is unbiased and its variance is analytically studied.
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.
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.
Proposes ESCFR to estimate treatment effects from biased data.
problem Treatment selection bias in observational data.
method Stochastic optimal transport with relaxed mass-preserving and proximal factual outcome regularizers.
result Significantly better performance in estimating treatment effects.
The estimation of treatment effects is a pervasive problem in medicine. Existing methods for estimating treatment effects from longitudinal observational data assume that there are no hidden confounders, an assumption that is not testable in practice and, if it does not hold, leads to biased estimates. In this paper, w…
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.
The empirical practice of using factor models to adjust for shared, unobserved confounders, , in observational settings with multiple treatments, , is widespread in fields including genetics, networks, medicine, and politics. Wang and Blei (2019, WB) formalizes these procedures and develops the …
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.
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.
The Rashomon effect shows many models can perform similarly, explored in this paper.
problem Why do many models perform similarly in machine learning?
method Categorized causes into statistical, structural, and procedural sources.
result Structural multiplicity persists and cannot be resolved without additional assumptions.
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.
Individual risk models need to capture possible correlations as failing to do so typically results in an underestimation of extreme quantiles of the aggregate loss. Such dependence modelling is particularly important for managing credit risk, for instance, where joint defaults are a major cause of concern. Often, the d…
A method estimates causal parameters using a latent variable recovery.
problem Estimating causal parameters in contexts with multiple causes and unobserved confounding.
method Substitute adjustment via recovery of latent variables.
result Substitute adjustment estimates adjusted regression parameters under certain conditions.
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.
Observed associations in a database may be due in whole or part to variations in unrecorded (latent) variables. Identifying such variables and their causal relationships with one another is a principal goal in many scientific and practical domains. Previous work shows that, given a partition of observed variables such …
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.
Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulationstat.ML
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.
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.
Estimates peer influence effects using embeddings for social networks.
problem Estimating peer contagion effects in social networks with unobserved confounders.
method Nonparametrically adjusts for homophily using node embeddings.
result Formalizes causal effect of peer influence in social networks.
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.
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.
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributionsstat.ML
We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs from all the individual training distributions. This covariate shift is caused, in part, due to unobserved features in the datasets. The objec…
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.
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 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.
The paper shows how to audit fairness in decisions with hidden risk factors.
problem Estimating fairness in decisions influenced by hidden, unobservable risk factors.
method Derives unbiased estimates of risk using historical data and audits existing decision-making systems.
result One can compute meaningful bounds on treatment rates for high-risk individuals, even with hidden confounders.
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confoundingstat.ME
Develops Austen plots for assessing bias from unobserved confounding in observational studies.
problem Bias in causal estimates due to unobserved confounding.
method Formalizes confounding strength, uses Austen plots to visualize and quantify bias.
result Allows domain experts to assess the plausibility of strong confounders.
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.
A new algorithm CAP learns optimal policies from observational data with confounding bias and missing observations.
problem Offline contextual bandit with confounding bias and missing observations.
method CAP policy learning, forming reward function as solution of integral equation system, building confidence set, and greedily taking action with pessimism.
result Developed an upper bound to the suboptimality of CAP for the offline contextual bandit problem.