arXiv research

Scalable GPLVM reduces complexity in scRNA-seq data, accounting for technical and biological confounders.

High-dimensional data acquired from biological experiments such as next generation sequencing are subject to a number of confounding effects. These effects include both technical effects, such as variation across batches from instrument noise or sample processing, or institution-specific differences in sample acquisiti…

We propose a method for inferring the existence of a latent common cause ('confounder') of two observed random variables. The method assumes that the two effects of the confounder are (possibly nonlinear) functions of the confounder plus independent, additive noise. We discuss under which conditions the model is identi…

Develops a method for causal inference with noisy confounders.

Paper proposes a policy gradient method for confounded POMDPs.

This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for an action is modeled as a linear function of known action features confounded by an non-linear action-independent term. We design new algorithms that achieve $\tilde{O}(d\sqrt{T})$ regret …

New test for point processes without strong model assumptions.

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…

The paper proposes a method to precisely decompose confounders and estimate treatment effects.

Research shows continual learning challenges in confounded datasets.

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 …

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…

Detect hidden confounding in observational data using multiple environments.

Machine learning confound removal biases results, leading to misleading predictions.

KRCD detects unobserved confounders in nonlinear observational data.

Study identifies conditions for proxy adjustment in confounded binary treatment outcomes.

New method estimates policy performance under unobserved confounding.

Consistent estimator derived for confounding strength in observational data.

A new method uses randomized trials to estimate the strength of unobserved confounding.

This paper tackles confounding biases in data augmentation.

New method recovers latent confounders from high-dimensional proxy variables.

Proposes tests to control confounding bias in predictive models.

New method tackles confounded bandit problems with dual instrumental variables.

New approach for causal inference with interdependent, time-varying latent confounders.

Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal f…

Estimating the individual treatment effect (ITE) from observational data is essential in medicine. A central challenge in estimating the ITE is handling confounders, which are factors that affect both an intervention and its outcome. Most previous work relies on the unconfoundedness assumption, which posits that all th…

CARE improves LLM aggregation by accounting for shared confounders.

Proposes BDCM to handle unmeasured confounders in causal inference.

New methods prioritize acquiring confounding features for efficient treatment effect estimation.

Valid causal inference with unobserved confounding in high-dimensional settings.

Spectral deconfounding improves machine learning models by reducing hidden confounding effects.

Study shows how adjusting for a binary proxy can bound causal effects.

Paper tackles unobserved confounding in human-AI collaborations.

SpaCE tackles spatial confounding in scientific studies.

Estimates causal effects with selection bias and confounding using regression.

Paper tackles backwards-compatible data adaptation for confounded covariate and label shifts.

Deep CITs test conditional independence in images, improving brain MRI scan analysis.

In this paper, we study the confounder detection problem in the linear model, where the target variable $Y$ is predicted using its $n$ potential causes $X_n=(x_1,...,x_n)^T$. Based on an assumption of rotation invariant generating process of the model, recent study shows that the spectral measure induced by the regress…

Proposes DSW for unbiased ITE estimation with dynamic confounders.

Paper uses Gaussian processes to handle shared latent confounders in causal inference.

Study linear contextual bandits with confounded offline data, improving regret bounds.

FL-Sailer enables federated learning for scATAC-seq data, reducing dimensionality and noise.

We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression leve…

With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images. To gain a better understanding of how a disease impacts the brain, the studies visualize the salience maps of the ConvNet highlighting voxels within the brain major…

Estimates CATE under hidden confounding, accounting for bias and ignorance.

New methods for handling confounding in observational studies.

SDD improves DD for estimating treatment effects by adjusting for confounding.

Method estimates CATE using RCT data to handle hidden confounders.