arXiv research

New method learns functions without paired data using mediating variables.

Dirac-harmonic maps are uncoupled under certain conditions.

Uncoupled regression is the problem to learn a model from unlabeled data and the set of target values while the correspondence between them is unknown. Such a situation arises in predicting anonymized targets that involve sensitive information, e.g., one's annual income. Since existing methods for uncoupled regression …

Paper establishes identifiability and achievability for causal representation learning.

New algorithms converge faster to Nash equilibrium in zero-sum games with bandit feedback.

Study uncoupled solutions to Dirac-Yang-Mills equations on spin manifolds.

MediEncoder learns nonlinear representations for causal mediation analysis.

Paper proposes a machine learning-based method for estimating mediation effects.

A new RL framework evaluates dynamic mediation effects over time.

Efficient algorithm converges to Nash equilibrium in bilinear problems with bandit feedback.

A method combines deep learning and G-estimation for causal mediation analysis.

A new DML method for continuous treatments uncovers causal mediation effects.

Study compares estimators for causal mediation analysis with multiple mediators.

Study introduces a new method to estimate causal mediation with multiple mediators.

Tests whether a treatment's effect is fully mediated by observed outcomes and identifies causal mechanisms.

DCMA uses generative models to analyze treatment effects on entire outcome distributions.

Multi-domain translation seeks to learn a probabilistic coupling between marginal distributions that reflects the correspondence between different domains. We assume that data from different domains are generated from a shared latent representation based on a structural equation model. Under this assumption, we show th…

DCMA uses generative models to analyze complex treatment effects on outcome distributions.

New method analyzes complex multivariate pathways in high-dimensional data.

New method handles indirect mediators in CMA for complex scenarios.

PESCAL uses mediators to learn from confounded offline data.

DeepMed uses DNNs to estimate causal mediation effects without sparsity constraints.

New method stabilizes IF-based estimators for causal mediation analysis with continuous mediators.

Extends causal inference to hidden mediators with proxies.

Isotonic regression is a standard problem in shape-constrained estimation where the goal is to estimate an unknown nondecreasing regression function $f$ from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots n$. While this problem is well understood both statistically and computationally, much l…

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

An assistant learns to mediate decisions between humans and experts, balancing risk and learning.

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but wheth…

Proposes new method to handle hidden confounders in causal mediation analysis.

A new method for identifying the best arm in multi-armed bandits with mediator feedback.

It becomes increasingly popular to perform mediation analysis for complex data from sophisticated experimental studies. In this paper, we present Granger Mediation Analysis (GMA), a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imagin…

In many scientific studies, it becomes increasingly important to delineate the causal pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate the pathway effects, commonly expressed as products of coefficients. However, it …

The paper addresses causal mediation analysis with post-treatment events, proposing robust estimators and efficient methods.

Proximal Mediation Analysis with Hidden Recanting Witnesses

Proposes estimators for complex dose-response curves using kernel methods.

Node2Grids uncouples GCN training for large graphs, saving memory and computation.

RANDomized-exploration policy Optimization via Multiple Importance Sampling with Truncation (RANDOMIST) for PO with mediator feedback.

Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.

Study decomposes racial healthcare disparities via shifts in mediator distributions.

We study Dirac-harmonic maps from surfaces to manifolds with torsion, which is motivated from the superstring action considered in theoretical physics. We discuss analytic and geometric properties of such maps and outline an existence result for uncoupled solutions.

The paper compares and optimizes estimators for treatment effects with observed confounders and mediators.

We prove existence results for Dirac-harmonic maps using index theoretical tools. They are mainly interesting if the source manifold has dimension 1 or 2 modulo 8. Our solutions are uncoupled in the sense that the underlying map between the source and target manifolds is a harmonic map.

M-learner estimates treatment effects in mediation models with subgroup identification.

Game-theoretic models of learning are a powerful set of models that optimize multi-objective architectures. Among these models are zero-sum architectures that have inspired adversarial learning frameworks. An important shortcoming of these zeros-sum architectures is that gradient-based training leads to weak convergenc…

Develops a new density ratio estimator for causal inference.

Unified framework for estimating indirect effects in observational studies with unmeasured confounding.

Estimates causal effects using machine learning for binary treatment and mediator.

The paper identifies the best treatment to maximize NDPO, a key outcome in causal mediation analysis.