Zobrazeno 1 - 10
of 1 268
pro vyhledávání: '"KENNEDY, EDWARD"'
Autor:
Levis, Alexander W., Kennedy, Edward H., McClean, Alec, Balakrishnan, Sivaraman, Wasserman, Larry
Recent methodological research in causal inference has focused on effects of stochastic interventions, which assign treatment randomly, often according to subject-specific covariates. In this work, we demonstrate that the usual notion of stochastic i
Externí odkaz:
http://arxiv.org/abs/2411.14285
Understanding treatment effect heterogeneity is vital for scientific and policy research. However, identifying and evaluating heterogeneous treatment effects pose significant challenges due to the typically unknown subgroup structure. Recently, a nov
Externí odkaz:
http://arxiv.org/abs/2411.01250
Causal inference problems often involve continuous treatments, such as dose, duration, or frequency. However, identifying and estimating standard dose-response estimands requires that everyone has some chance of receiving any level of the exposure (i
Externí odkaz:
http://arxiv.org/abs/2409.11967
Autor:
Rubio, Mateo Dulce, Kennedy, Edward
We contribute a general and flexible framework to estimate the size of a closed population in the presence of $K$ capture-recapture lists and heterogeneous capture probabilities. Our novel identifying strategy leverages the fact that it is sufficient
Externí odkaz:
http://arxiv.org/abs/2407.03539
In causal inference, sensitivity models assess how unmeasured confounders could alter causal analyses, but the sensitivity parameter -- which quantifies the degree of unmeasured confounding -- is often difficult to interpret. For this reason, researc
Externí odkaz:
http://arxiv.org/abs/2405.08738
Optimal treatment rules are mappings from individual patient characteristics to tailored treatment assignments that maximize mean outcomes. In this work, we introduce a conditional potential benefit (CPB) metric that measures the expected improvement
Externí odkaz:
http://arxiv.org/abs/2405.08727
We study the problem of constructing an estimator of the average treatment effect (ATE) that exhibits doubly-robust asymptotic linearity (DRAL). This is a stronger requirement than doubly-robust consistency. A DRAL estimator can yield asymptotically
Externí odkaz:
http://arxiv.org/abs/2405.08525
Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup structure is typically unknown, it is more challenging to
Externí odkaz:
http://arxiv.org/abs/2405.03083
When estimating causal effects from observational studies, researchers often need to adjust for many covariates to deconfound the non-causal relationship between exposure and outcome, among which many covariates are discrete. The behavior of commonly
Externí odkaz:
http://arxiv.org/abs/2405.00118
With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the individual gene expression levels
Externí odkaz:
http://arxiv.org/abs/2404.09119