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CAUSALab (@CausalInference)

CausalInference

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R
50.0 %

SAS 35.7 %

Python
14.3 %

gfoRmula

The gfoRmula package implements the parametric g-formula in R. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean of an outcome under hypothetical treatment strategies specified by the user.

GFORMULA-SAS

The GFORMULA macro implements the parametric g-formula in SAS. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean of an outcome under hypothetical treatment strategies specified by the user.

pygformula

The pygformula implements the parametric g-formula in Python. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean of an outcome under hypothetical treatment strategies specified by the user.

Simulation-Scenarios-JAMA

code for article in JAMA

CompetingEvents_Young_SIM_2020

R Code used in https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8471

CAUSALab_Papers

Software used for papers published by CAUSALab members

gfoRmula-benchmark

Code and data used in the benchmark in "gfoRmula: An R package for estimating the effects of sustained treatment strategies via the parametric g-formula"

IV-Bounds

GFORMULA-RCT-SAS

MSM-SAS

pytruncreg

Estimation of Gaussian Truncated Regression Models

Resource_Constrained_dynMSM_Simulation

volume-patient

Paper and code for volume-outcomes analysis among patients undergoing pancreatectomy

bariatric

Analytic code for bariatric analysis including synthetic data