This paper proposes a method for identifying and estimating conditional principal causal effects with heterogeneity within strata under principal ignorability, using a novel doubly cross-fit doubly ro…

The paper proposes a new, optimal estimator for semiparametric inference that improves upon standard double machine learning (DML) rates by eliminating the first-order stochastic error of nuisance fun…

This paper introduces ATLAS, a method for disentangling invariant and heterogeneous factors in multi-environment factor models, enabling transferable prediction and robust invariant-factor-only predic…

The paper proposes a novel, practical upper bound to estimate the worst-case performance of medical prediction models on the target population, even when the selection bias mechanism and target data a…

The paper proposes a novel, computationally efficient estimator for estimating heterogeneous treatment effects in panel data by framing the problem as matrix completion and establishing a sharp row-wi…

This paper introduces a new approach for scalable causal discovery in directed acyclic graphs (DAGs) with clustered data and local cluster-level effects.

This paper introduces a new R package for Non-negative Matrix Factorization (NMF) and compares its performance systematically with two other R packages using real-world data.

This paper introduces topological-geometrical metrics to estimate structural causal effects that are missed by traditional mean-based methods, proposing a new concept called topological ignorability.

This paper introduces Distributional Random Forests, which replace mean-based CART splitting with criteria that compare full conditional response distributions in candidate children. The authors syste…

This paper proposes a novel estimator for the target linear coefficient in a partial linear model with black-box nuisance estimation and establishes its unimprovable error rate.

The paper proposes a Bayesian meta-learner to accurately predict the distribution of Alzheimer's disease progression scores for individuals, outperforming existing methods, especially for long-term pr…