arXiv research

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

New method estimates treatment effects from high dimensional data.

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

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

CausalEGM estimates causal effects by encoding confounders, improving performance in high-dimensional settings.

DFPV improves PCL for confounded bandit policy evaluation.

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

New DR method improves robustness in high-dimensional treatment effects.

Framework tests CATE homogeneity across trials and evaluates confounding.

Paper develops a new estimator for dynamic treatment effects in high-dimensional settings.

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…

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…

Proposes AAA for efficient association estimation with confounders.

Self-Distilled Disentanglement improves counterfactual predictions by separating variables.

New approach predicts under latent shifts using high-dimensional images.

New method scores DAGs by identifying unobserved confounding.

New methods estimate causal effects through mediators, handling confounding without strict assumptions.

Adapts causal inference for high-dimensional treatments like text strings.

CATR rationalizes text data to stabilize causal effect estimation.

MOCA uses modular attention to estimate causal effects from complex data.

The study uses pre-trained neural networks to adjust for confounding in non-tabular data.

S-DIDML integrates structural DID with ML for causal inference in high-dimensional data.

Method estimates causal effects from combined interventional and observational data.

Develops scalable methods to assess sensitivity and uncertainty in continuous treatment effects.

Study interpolating estimators for causal learning from observational data.

New method debiases counterfactual distributions using observational data.

BGM-IV uses AI to estimate causal effects in complex data.

New method samples from any causal effect given conditional generative models.

Proposes Causal-Batle for estimating treatment effects in small high-dimensional datasets.

Paper proposes mechanism learning to reverse causal inference in ML.

New method combines strengths of two PCL approaches without density ratio estimation.

How can we understand classification decisions made by deep neural networks? Many existing explainability methods rely solely on correlations and fail to account for confounding, which may result in potentially misleading explanations. To overcome this problem, we define the Causal Concept Effect (CaCE) as the causal e…

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

With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic pr…

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…

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.

CausalBGM uses AI to infer causal effects from complex data.

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

Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard methods such as 2SLS, GMM, and more recent variants are significantly impeded when the causal effects are complex, the instruments are high-dimen…

Deep Bayesian models estimate causal effects for dynamic treatment regimes over long follow-up times.