New method estimates treatment-response curves with covariate and timing measurement errors.
problem Estimating treatment impact on continuous temporal response with covariate measurement errors.
method Combines parametric response functions and sparse Gaussian process for baseline trend, considering both treatment covariates and timing errors.
result Significant improvements in estimation accuracy and prediction for diet impact on blood glucose measurements.
We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by applications where response varies by individuals and therefore, estimating resp…
Treatment effects can be estimated from observational data as the difference in potential outcomes. In this paper, we address the challenge of estimating the potential outcome when treatment-dose levels can vary continuously over time. Further, the outcome variable may not be measured at a regular frequency. Our propos…
CausalLongPFN predicts counterfactual outcomes from time-series data.
problem Predicting future outcomes under varying treatments in time-series data with confounding and heterogeneity.
method Prior-fitted network pretrained on synthetic episodes of temporal structural causal models.
result CausalLongPFN outperforms domain-trained models on factual and counterfactual prediction tasks.
Machine learning improves glioma diagnosis and prognosis.
problem Improving glioma diagnosis and prognosis using imaging biomarkers.
method Search PubMed and MEDLINE for articles applying machine learning to high-grade glioma biomarkers.
result Machine learning enables accurate classification of glioma biomarkers.
Optimum in Convex Hulls (OCH) generalizes clinical trial results to broader populations.
problem Clinical trials exclude confounding but limit recruitment; observational data are more inclusive but suffer from confounding.
method OCH uses convex hulls of conditional expectations or densities to approximate the true treatment effect from both observational and trial data.
result OCH estimates the treatment effect with state-of-the-art accuracy in terms of both expectations and densities.
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.
Study shows auditing fairness of personalized interventions is impossible due to unknown ground truths.
problem Auditing fairness of personalized interventions in social services, education, and healthcare.
method Point-identification of quantities under monotone treatment response assumption, providing sensitivity analysis for violations.
result Proves impossibility of auditing fairness using standard metrics and provides methods for auditing using partially-identified ROC and xROC curves.
Scientists develop a model to identify treatment responders from non-responders.
problem Analyzing samples that respond to treatment in studies.
method Causal two-groups (C2G) model, empirical Bayes procedures.
result The C2G model controls false discovery rate and has near-optimal power.
The paper develops a causal machine learning framework to optimize aid allocation.
problem Optimizing aid allocation to reduce new HIV infections in poor countries.
method The framework uses a balancing autoencoder, counterfactual generator, and inference model to predict heterogeneous treatment effects.
result The framework predicts a reduction of up to 3.3% in new HIV infections, saving 50,000 lives.
EB-VAE combines tumor growth and dropout data for personalized treatment response modeling.
problem Challenges in integrating longitudinal tumor measurements, dropout information, and genetic covariates.
method Extended EB-VAE framework to jointly model longitudinal and time-to-event data, incorporating dropout hazard and genetic covariates.
result Hybrid decoder formulation yields consistent treatment-effect parameters and prior predictive performance comparable to neural decoder.
MFM integrates multiple evolving populations using Wasserstein manifold flows.
problem Learning dynamics of multiple interacting populations evolving over time.
method Meta Flow Matching (MFM) integrates vector fields on Wasserstein manifold using amortized flow models and GNN embeddings.
result MFM improves prediction of individual treatment responses on multi-patient single-cell drug screen data.
We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction. Our models, Perturbation Variational Autoencoder and its semi-supervised extension, Drug Response Variational Autoencoder (Dr.VAE), learn latent representation of the underlying gene states befor…
New method estimates effects of multiple nutrients on blood glucose.
problem Estimating physiological response to multiple nutrient treatments.
method Convolution-based multi-output Gaussian process model.
result Improved prediction accuracy and better interpretation of individual nutrient effects.
A new framework for causal inference in networked settings.
problem Causal inference under network interference.
method Characterize agent network configuration and use it to estimate treatment effects.
result Finite-sample bounds and asymptotically valid tests for policy irrelevance.
Paper models treatment effects by clustering patients with distinct survival characteristics.
problem Estimating treatment efficacy in clinical settings with censored outcomes.
method Latent variable approach to model heterogeneous treatment effects.
result The latent structure can mediate base survival rates and reveal actionable phenotypes.
Estimates individualized treatment effects using shared RBF-net neurons.
problem Identifying differential treatment effects based on covariates.
method Non-parametric radial basis function (RBF)-nets with shared hidden neurons in a Bayesian framework.
result Demonstrated through simulations and real data, the method identifies interesting 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…
Many problems that appear in biomedical decision making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The costs of false positives and false negatives vary across application domains and receiver operating characteristic (ROC) curves provide a visu…
PKB framework boosts genomic data analysis by integrating pathway knowledge.
problem Boosting discovery power and connecting new findings with biological mechanisms in genomic data.
method Pathway-based Kernel Boosting (PKB) framework integrating clinical and pathway information for prediction of various outcomes.
result PKB substantially outperforms other methods in predicting drug response and cancer survival.
This work improves deep learning models for fMRI by generating realistic brain morphology images.
problem Limited dataset sizes for functional MRI limit the accuracy of deep learning models.
method Proposes a method to generate new fMRI images with realistic brain morphology.
result Demonstrates a 26% improvement in predicting antidepressant treatment response using augmented images.
Study causal inference under specific sampling methods with monotonicity assumptions.
problem Causal inference under biased sampling methods.
method Binary-outcome and binary-treatment case study with monotonicity assumptions.
result Monotonicity assumptions yield comparable results to random sampling.
A method models continuous-time glucose distributions in children with diabetes.
problem Capturing subtle temporal changes in glucose distributions.
method Probabilistic framework using Gaussian mixtures and neural ODEs.
result Detects treatment-related improvements in glucose dynamics.
Estimates personalized treatment response curves using covariates.
problem Flexible estimation of personalized treatment response curves.
method Sieve based nonparametric estimator of smoothed regimen-response curve function.
result Asymptotic linearity and undersmoothing criteria for efficient estimation.
Proposes a novel method to cluster individuals based on treatment effects.
problem Identifying subpopulations with different treatment responses.
method Clusters individuals using a learned kernel derived from causal forests, revealing latent subgroup structures.
result Captures meaningful treatment effect heterogeneity through kernelized clustering.
This work addresses causal inference challenges in networked interference and proposes GNN-based estimators for individual treatment effects.
problem Estimating individual treatment effects in randomized experiments with networked interference.
method Uses Graph Neural Networks (GNNs) to capture network dependencies and derive causal effect estimators.
result Provides policy regret bounds and heuristic error bounds for GNN-based causal estimators under network interference and treatment capacity constraints.
The paper proposes a method to estimate heterogeneous treatment effects using pretraining strategies.
problem Estimating conditional average treatment effects (CATE) in the presence of many covariates.
method The approach leverages prognostic factors that also predict treatment effect heterogeneity, using the R-learner framework.
result The proposed method improves estimation accuracy and power for detecting treatment effect heterogeneity.
Three approaches learn personalized treatment policies for UTI patients.
problem Learning optimal treatment policies in multiobjective settings with fully observed outcomes.
method Indirect and direct approaches using predictive models and without intermediate models.
result All approaches outperform clinicians in achieving better performance on all outcomes and trade-offs.
CAST models time-varying treatment effects in cancer patients.
problem Estimating treatment effects at fixed time points limits understanding of dynamic changes over time.
method CAST combines parametric and non-parametric methods to model continuous time-varying treatment effects.
result CAST reveals how treatment effects rise, peak, and decline over the follow-up period.
Expands causal clustering framework with hierarchical and density-based methods.
problem Identifying heterogeneous treatment effects in unknown subgroup structure.
method Integrates hierarchical and density-based clustering algorithms into causal k-means clustering.
result Plug-in estimators for causal clustering are simple and readily implementable.
High-dimensional linear classifiers, such as the support vector machine (SVM) and distance weighted discrimination (DWD), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. However, their use is limited to applications where a single vector of features is mea…
Retina-VAE models macular disease spectrum using clinical data.
problem Representing the spectrum of macular diseases clinically.
method Variational autoencoder (VAE) model trained on patient profiles.
result Latent vectors cluster into 14 subtypes, suggesting treatment responses.
Develops methods for causal inference in longitudinal data.
problem Estimating Individual Treatment Effects (ITEs) in high-dimensional, time-varying data.
method Causal Dynamic Variational Autoencoder (CDVAE) and long-term counterfactual regression framework.
result CDVAE outperforms baselines and improves state-of-the-art models, approaching oracle performance.
AI framework uses multi-omics data to personalize cancer treatment suggestions.
problem Leveraging AI for personalized cancer treatment based on complex patient characteristics.
method Modular machine learning framework trained on diverse multi-omics technologies.
result Superior performance in personalized counterfactual treatment suggestions.
Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and gen…
LOT framework embeds high-dimensional cell data into interpretable Euclidean space.
problem Lack of interpretable methods for high-dimensional cell data.
method Adapts Linear Optimal Transport (LOT) to irregular point clouds.
result Accurate and interpretable classification and synthetic data generation.
Machine learning improves concussion diagnosis in female athletes.
problem Limited effectiveness of traditional concussion diagnosis methods for female athletes.
method Advanced neuroinformatics and machine learning models.
result Improved understanding of concussion mechanisms in female athletes.
MISTR improves HTE estimation in survival data with heavy censoring and instrumental variables.
problem Estimating HTE in survival data with censoring and unobserved confounders.
method MISTR uses recursively imputed survival trees to handle censoring and instrumental variables.
result MISTR outperforms existing methods under heavy censoring and instrumental variable settings.
Study benchmarks contextual bandit algorithms for precision oncology using in vitro data.
problem Designing effective protocols for individual treatment assignment in precision oncology.
method Proposed a benchmark dataset of in vitro drug responses to evaluate contextual bandit algorithms.
result Bayesian bandit algorithms performed better than a rule-based baseline in minimizing regret.
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
problem Heterogeneous disease progression and treatment response in Parkinson's Disease.
method Two-stage conformal prediction framework with statistical guarantees.
result Quantifies uncertainty in medication needs predictions, improving clinical trust and quality of life.