Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Schuler, Alejandro"'
In environmental epidemiology, identifying subpopulations vulnerable to chemical exposures and those who may benefit differently from exposure-reducing policies is essential. For instance, sex-specific vulnerabilities, age, and pregnancy are critical
Externí odkaz:
http://arxiv.org/abs/2406.10792
The average treatment effect (ATE) is a common parameter estimated in causal inference literature, but it is only defined for binary treatments. Thus, despite concerns raised by some researchers, many studies seeking to estimate the causal effect of
Externí odkaz:
http://arxiv.org/abs/2405.07109
Mediation analysis in causal inference typically concentrates on one binary exposure, using deterministic interventions to split the average treatment effect into direct and indirect effects through a single mediator. Yet, real-world exposure scenari
Externí odkaz:
http://arxiv.org/abs/2307.02667
Although randomized controlled trials (RCTs) are a cornerstone of comparative effectiveness, they typically have much smaller sample size than observational studies because of financial and ethical considerations. Therefore there is interest in using
Externí odkaz:
http://arxiv.org/abs/2305.19180
This study introduces a nonparametric definition of interaction and provides an approach to both interaction discovery and efficient estimation of this parameter. Using stochastic shift interventions and ensemble machine learning, our approach identi
Externí odkaz:
http://arxiv.org/abs/2305.01849
Traditional regulations of chemical exposure tend to focus on single exposures, overlooking the potential amplified toxicity due to multiple concurrent exposures. We are interested in understanding the average outcome if exposures were limited to fal
Externí odkaz:
http://arxiv.org/abs/2302.07976
Covariate adjustment and methods of incorporating historical data in randomized clinical trials (RCTs) each provide opportunities to increase trial power. We unite these approaches for the analysis of RCTs with binary outcomes based on the Cochran-Ma
Externí odkaz:
http://arxiv.org/abs/2212.09903
Gradient boosting performs exceptionally in most prediction problems and scales well to large datasets. In this paper we prove that a ``lassoed'' gradient boosted tree algorithm with early stopping achieves faster than $n^{-1/4}$ L2 convergence in th
Externí odkaz:
http://arxiv.org/abs/2205.10697
Autor:
Schuler, Alejandro
In randomized trials with continuous-valued outcomes the goal is often to estimate the difference in average outcomes between two treatment groups. However, the outcome in some trials is longitudinal, meaning that multiple measurements of the same ou
Externí odkaz:
http://arxiv.org/abs/2108.06621
Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. However, the options are much more limited when the prediction target is mu
Externí odkaz:
http://arxiv.org/abs/2106.03823