Statistical Methods and Inference
209 papers on this topic
A Statistical Theory of Regularization-Based Continual Learning
We provide a statistical analysis of regularization-based continual learning on a sequence of linear regression tasks, with emphasis on how different regularization terms affect the model...
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
The ability to understand causality from data is one of the major milestones of human-level intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships among the...
Enhancing Conformal Prediction Using E-Test Statistics
Conformal Prediction (CP) serves as a robust framework that quantifies uncertainty in predictions made by Machine Learning (ML) models. Unlike traditional point predictors, CP generates...
Diffusion Models for Causal Discovery via Topological Ordering
Discovering causal relations from observational data becomes possible with additional assumptions such as considering the functional relations to be constrained as nonlinear with additive noise...
Adjustment for Confounding using Pre-Trained Representations
There is growing interest in extending average treatment effect (ATE) estimation to incorporate non-tabular data, such as images and text, which may act as sources of confounding. Neglecting these...
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of dimensionality,...
SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis
Survival analysis, which estimates the probability of event occurrence over time from censored data, is fundamental in numerous real-world applications, particularly in high-stakes domains such as...
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
Representing uncertainty in causal discovery is a crucial component for experimental design, and more broadly, for safe and reliable causal decision making. Bayesian Causal Discovery (BCD) offers...
SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph
Causal discovery can be computationally demanding for large numbers of variables. If we only wish to estimate the causal effects on a small subset of target variables, we might not need to learn...
Local Causal Discovery for Statistically Efficient Causal Inference
Causal discovery methods can identify valid adjustment sets for causal effect estimation for a pair of target variables, even when the underlying causal graph is unknown. Global causal discovery...
Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method
The number of artificial intelligence algorithms for learning causal models from data is growing rapidly. Most ``causal discovery'' or ``causal structure learning'' algorithms are primarily...
Scalable Causal Discovery from Recursive Nonlinear Data via Truncated Basis Function Scores and Tests
Learning graphical conditional independence structures from nonlinear, continuous or mixed data is a central challenge in machine learning and the sciences, and many existing methods struggle to...
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine learning to relax...
Cross-Validation Conformal Risk Control
Conformal risk control (CRC) is a recently proposed technique that applies post-hoc to a conventional point predictor to provide calibration guarantees. Generalizing conformal prediction (CP),...
A Sample Efficient Conditional Independence Test in the Presence of Discretization
In many real-world scenarios, interested variables are often represented as discretized values due to measurement limitations. Applying Conditional Independence (CI) tests directly to such...
Optimal rates for density and mode estimation with expand-and-sparsify representations
Expand-and-sparsify representations are a class of theoretical models that capture sparse representation phenomena observed in the sensory systems of many animals. At a high level, these...
CenTime: Event-Conditional Modelling of Censoring in Survival Analysis
Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to...
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
Random forests (RFs) are among the most popular supervised learning algorithms due to their nonlinear flexibility and ease-of-use. However, as black box models, they can only be interpreted via...