Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Bradić, Jelena"'
Autor:
Zhang, Yuqian, Bradic, Jelena
Deep neural networks (DNNs) have demonstrated remarkable empirical performance in large-scale supervised learning problems, particularly in scenarios where both the sample size $n$ and the dimension of covariates $p$ are large. This study delves into
Externí odkaz:
http://arxiv.org/abs/2407.08560
In this paper, we propose a new random forest algorithm that constructs the trees using a novel adaptive split-balancing method. Rather than relying on the widely-used random feature selection, we propose a permutation-based balanced splitting criter
Externí odkaz:
http://arxiv.org/abs/2402.11228
In this paper we address the challenges posed by non-proportional hazards and informative censoring, offering a path toward more meaningful causal inference conclusions. We start from the marginal structural Cox model, which has been widely used for
Externí odkaz:
http://arxiv.org/abs/2311.07752
We consider a general proportional odds model for survival data under binary treatment, where the functional form of the covariates is left unspecified. We derive the efficient score for the conditional survival odds ratio given the covariates using
Externí odkaz:
http://arxiv.org/abs/2310.14448
The Decaying Missing-at-Random Framework: Doubly Robust Causal Inference with Partially Labeled Data
In real-world scenarios, data collection limitations often result in partially labeled datasets, leading to difficulties in drawing reliable causal inferences. Traditional approaches in the semi-supervised (SS) and missing data literature may not ade
Externí odkaz:
http://arxiv.org/abs/2305.12789
Estimating dynamic treatment effects is essential across various disciplines, offering nuanced insights into the time-dependent causal impact of interventions. However, this estimation presents challenges due to the "curse of dimensionality" and time
Externí odkaz:
http://arxiv.org/abs/2111.06818
Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders. Doubly robust (DR) approaches have emerged as promising tools for estimating treatment effects due to thei
Externí odkaz:
http://arxiv.org/abs/2110.04924
Publikováno v:
Information and Inference: A Journal of the IMA (2023), Vol. 12, No. 3, 2066-2159
Semi-supervised (SS) inference has received much attention in recent years. Apart from a moderate-sized labeled data, L, the SS setting is characterized by an additional, much larger sized, unlabeled data, U. The setting of |U| >> |L|, makes SS infer
Externí odkaz:
http://arxiv.org/abs/2104.06667
Autor:
Bradic, Jelena, Zhu, Yinchu
Breiman challenged statisticians to think more broadly, to step into the unknown, model-free learning world, with him paving the way forward. Statistics community responded with slight optimism, some skepticism, and plenty of disbelief. Today, we are
Externí odkaz:
http://arxiv.org/abs/2103.11327
Dynamic covariate balancing: estimating treatment effects over time with potential local projections
Autor:
Viviano, Davide, Bradic, Jelena
This paper studies the estimation and inference of treatment histories in panel data settings when treatments change dynamically over time. We propose a method that allows for (i) treatments to be assigned dynamically over time based on high-dimensio
Externí odkaz:
http://arxiv.org/abs/2103.01280