7 papers · 1 filter
The positivity assumption in causal mediation analyses? Checked!
Arthur Chatton, Geneviève Lefebvre, Mireille E. Schnitzer +1
Causal mediation analyses are increasingly used in psychological sciences. Among the required assumptions, positivity is unfortunately seldom mentioned, likely due to the lack of t…
Assumption-Lean Differential Variance Inference for Heterogeneous Treatment Effect Detection
Philippe A. Boileau, Hani Zaki, Gabriele Lileikyte +3
The conditional average treatment effect (CATE) is frequently estimated in clinical studies to refute a homogeneous treatment effect hypothesis. Under this regime, all patients mak…
Valid post-selection inference for penalized G-estimation
Ajmery Jaman, Ashkan Ertefaie, Michèle Bally +3
Understanding treatment effect heterogeneity is important for decision making in medical and clinical practices, or handling various engineering and marketing challenges. When deal…
Adaptive sparsening and smoothing of the treatment model for longitudinal causal inference using outcome-adaptive LASSO and marginal fused LASSO
Mireille E Schnitzer, Denis Talbot, Yan Liu +5
Causal variable selection in time-varying treatment settings is challenging due to evolving confounding effects. Existing methods mainly focus on time-fixed exposures and are not d…
Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes
Ajmery Jaman, Guanbo Wang, Ashkan Ertefaie +4
Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling these effect difference…
A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness under the Test-Negative Design: Analysis of Québec Administrative Data
Cong Jiang, Denis Talbot, Sara Carazo +1
The test-negative design (TND), which is routinely used for monitoring seasonal flu vaccine effectiveness (VE), has recently become integral to COVID-19 vaccine surveillance, notab…