Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Van der Laan, Lars"'
Inverse weighting with an estimated propensity score is widely used by estimation methods in causal inference to adjust for confounding bias. However, directly inverting propensity score estimates can lead to instability, bias, and excessive variabil
Externí odkaz:
http://arxiv.org/abs/2411.06342
In causal inference, many estimands of interest can be expressed as a linear functional of the outcome regression function; this includes, for example, average causal effects of static, dynamic and stochastic interventions. For learning such estimand
Externí odkaz:
http://arxiv.org/abs/2411.02771
We consider the problem of estimating the average treatment effect (ATE) when both randomized control trial (RCT) data and real-world data (RWD) are available. We decompose the ATE estimand as the difference between a pooled-ATE estimand that integra
Externí odkaz:
http://arxiv.org/abs/2405.07186
Autor:
van der Laan, Lars, Alaa, Ahmed M.
In machine learning, model calibration and predictive inference are essential for producing reliable predictions and quantifying uncertainty to support decision-making. Recognizing the complementary roles of point and interval predictions, we introdu
Externí odkaz:
http://arxiv.org/abs/2402.07307
We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and conditional relative risk. The EP-learning framework enjoys the same oracle-eff
Externí odkaz:
http://arxiv.org/abs/2402.01972
Foundation models are trained on vast amounts of data at scale using self-supervised learning, enabling adaptation to a wide range of downstream tasks. At test time, these models exhibit zero-shot capabilities through which they can classify previous
Externí odkaz:
http://arxiv.org/abs/2310.09926
Debiased machine learning estimators for nonparametric inference of smooth functionals of the data-generating distribution can suffer from excessive variability and instability. For this reason, practitioners may resort to simpler models based on par
Externí odkaz:
http://arxiv.org/abs/2307.12544
Autor:
Wang, Zeyi, van der Laan, Lars, Petersen, Maya, Gerds, Thomas, Kvist, Kajsa, van der Laan, Mark
Causal mediation analysis with random interventions has become an area of significant interest for understanding time-varying effects with longitudinal and survival outcomes. To tackle causal and statistical challenges due to the complex longitudinal
Externí odkaz:
http://arxiv.org/abs/2304.04904
Autor:
van der Laan, Lars, Gilbert, Peter B.
The aim of this manuscript is to explore semiparametric methods for inferring subgroup-specific relative vaccine efficacy in a partially vaccinated population against multiple strains of a virus. We consider methods for observational case-only studie
Externí odkaz:
http://arxiv.org/abs/2303.11462
We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a data-efficient variant of calibration that eliminates the need for hold
Externí odkaz:
http://arxiv.org/abs/2302.14011