CausalInference / 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.
☆23Updated 2 months ago
Alternatives and similar repositories for pygformula:
Users that are interested in pygformula are comparing it to the libraries listed below
- ☆13Updated 7 months ago
- Targeted Maximum Likelihood Estimation for a binary treatment: A tutorial. Statistics in Medicine. 2017☆16Updated 4 years ago
- R/haldensify: Highly Adaptive Lasso Conditional Density Estimation☆17Updated last month
- R package cfcausal☆27Updated 2 years ago
- 🎯 💯 Targeted Learning and Variable Importance for the Causal Effect of an Optimal Individualized Treatment Intervention☆12Updated 2 years ago
- Tutorial_Computational_Causal_Inference_Estimators☆31Updated 3 years ago
- An Interface to Specify Causal Graphs and Compute Balke Bounds☆16Updated last month
- R Package for "Matching on generalized propensity scores with continuous exposures". An innovative approach for estimating causal effects…☆30Updated 2 months ago
- Tools for estimating causal effects for multivariate continuous exposures☆13Updated 3 years ago
- Data for and description of the ACIC 2023 data competition☆32Updated last year
- R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects☆13Updated 10 months ago
- ☆31Updated 2 years ago
- Longitudinal Targeted Maximum Likelihood Estimation package☆23Updated last year
- Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted…☆24Updated 2 years ago
- Adaptive debiased machine learning of treatment effects with the highly adaptive lasso☆14Updated last year
- ☆14Updated last year
- ☆16Updated 3 years ago
- R package for doubly robust estimates of causal effects in high-dimensions using flexible Bayesian methods☆26Updated 2 months ago
- Publicly available code from my publications☆11Updated 10 months ago
- Website and blog for the research group of Mark J. van der Laan☆11Updated 3 years ago
- source code for personal website and blog☆13Updated 2 weeks ago
- R code to generate simulated data and implement the hierarchical TMLEs described in "A new approach to hierarchical data analysis: Target…☆9Updated 5 years ago
- ☆93Updated last year
- ☆10Updated 2 years ago
- R and python implementations of Accelerated Bayesian Causal Forest.☆25Updated 7 months ago
- 🎯🎓 Generalized Targeted Learning Framework☆37Updated 2 months ago
- ☆21Updated 3 weeks ago
- R package to compute distribution-free prediction bands using density estimators☆8Updated 3 years ago
- Doubly-Robust and Efficient Estimators for Survival and Ordinal Outcomes in RCTs Without Proportional Hazards or Odds Assumptions☆12Updated 3 years ago
- An R package to estimate the effect of interventions on univariate time series using ARIMA models☆19Updated 8 months ago