arXiv research

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

New algorithms bound treatment effects with unmeasured confounding.

SPOT improves differentiable causal discovery by estimating skeleton posterior for latent confounders.

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

We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities. We introduce σ-connection graphs (σ-CG), a new class of mixed graphs (containing undirected, bidirected…

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

Dynamic Structural Causal Models handle time-dependent systems with cycles and latent confounding.

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 …

Research shows continual learning challenges in confounded datasets.

GeoAdaLer enhances geometric understanding of Adam for stochastic optimization.

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…

Motivated by online recommendation and advertising systems, we consider a causal model for stochastic contextual bandits with a latent low-dimensional confounder. In our model, there are $L$ observed contexts and $K$ arms of the bandit. The observed context influences the reward obtained through a latent confounder var…

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.

Proposes ESCFR to estimate treatment effects from biased data.

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.

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…

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

Study compares causal discovery methods for cyclic models with hidden confounders.

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.

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.