3 citations · 4 across the 8 of their papers we have counts for
9 papers
Causal inference via propensity scores for case-control studies
Yan Liu, Anita Koushik, Philippe Boileau +5
Propensity score methods for causal inference are increasingly being used in cohort and experimental designs, but their development and uptake in outcome-dependent sampling schemes…
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…
trajmsm: An R package for Trajectory Analysis and Causal Modeling
Awa Diop, Caroline Sirois, Jason R. Guertin +3
The R package trajmsm provides functions designed to simplify the estimation of the parameters of a model combining latent class growth analysis (LCGA), a trajectory analysis techn…
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…
Efficient adjustment sets for time-dependent treatment effect estimation in nonparametric causal graphical model
David Adenyo, Mireille E Schnitzer, David Berger +2
Criteria for identifying optimal adjustment sets yielding consistent estimation with minimal asymptotic variance of average treatment effects in parametric and nonparametric models…
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…