GitHub topics: g-computation
dscolby/CausalELM.jl
Taking causal inference to the extreme!
Language: Julia - Size: 3.17 MB - Last synced at: 11 days ago - Pushed at: 6 months ago - Stars: 30 - Forks: 1
pzivich/zEpid
Epidemiology analysis package
Language: Python - Size: 10.9 MB - Last synced at: 10 days ago - Pushed at: about 2 years ago - Stars: 144 - Forks: 32
lorenzoFabbri/paper-helixSC-neuro
Main repository for the 3rd paper of my PhD.
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awamaeva/R-package-trajmsm
The R package trajmsm is based on the paper Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories: https://doi.org/10.48550/arXiv.2105.12720.
Language: R - Size: 485 KB - Last synced at: 10 months ago - Pushed at: over 1 year ago - Stars: 3 - Forks: 1
smn74/CIM_COVID-19
R code for the analyses conducted in Friedrich, S & Friede, T (2020). Causal inference methods for small non-randomized studies: Methods and an application in COVID-19. Submitted to Contemporary Clinical Trials.
Language: R - Size: 327 KB - Last synced at: almost 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0
ehsanx/TMLEworkshop
Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.
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