18 citations · 48 across the 12 of their papers we have counts for
14 papers
Sequential causal inference in a single world of connected units
Aurelien Bibaut, Maya Petersen, Nikos Vlassis +2
We consider adaptive designs for a trial involving N individuals that we follow along T time steps. We allow for the variables of one individual to depend on its past and on the pa…
Sufficient and insufficient conditions for the stochastic convergence of Cesàro means
Aurélien F. Bibaut, Alex Luedtke, Mark J. van der Laan
We study the stochastic convergence of the Cesàro mean of a sequence of random variables. These arise naturally in statistical problems that have a sequential component, where the…
Targeted Maximum Likelihood Estimation of Community-based Causal Effect of Community-Level Stochastic Interventions
Chi Zhang, Jennifer Ahern, Mark J. van der Laan
Unlike the commonly used parametric regression models such as mixed models, that can easily violate the required statistical assumptions and result in invalid statistical inference…
tmleCommunity: A R Package Implementing Target Maximum Likelihood Estimation for Community-level Data
Chi Zhang, Jennifer Ahern, Mark J. van der Laan +1
Over the past years, many applications aim to assess the causal effect of treatments assigned at the community level, while data are still collected at the individual level among i…
More Efficient Off-Policy Evaluation through Regularized Targeted Learning
Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis +1
We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have…
Fast rates for empirical risk minimization over càdlàg functions with bounded sectional variation norm
Aurélien F. Bibaut, Mark J. van der Laan
Empirical risk minimization over classes functions that are bounded for some version of the variation norm has a long history, starting with Total Variation Denoising (Rudin et al.…