Epidemiology analysis package

WeightIt: an R package for propensity score weighting

Taking Uncertainty Seriously: Bayesian Marginal Structural Models for Causal Inference in Political Science

📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation

Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data

📦 R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects

📦 🎲 R/medshift: Causal Mediation Analysis for Stochastic Interventions

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.

R package for estimating balancing weights using optimization

IPW- and CBPS-type propensity score reweighting, with various extensions (Stata package)

Tools for using marginal structural models (MSMs) to answer causal questions in developmental science.

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.

Code for assessing the causal effects of chemotherapy Received Dose Intensity (RDI) on survival outcomes in osteosarcoma patients using a Target Trial Emulation approach.

Inverse probability weighting for non-binary exposures. Simple example in Excel and SAS.

💬 Talk on causal inference and variable importance with stochastic interventions under two-phase sampling

Non-parametric variable selection and inference via the outcome-adaptive Random Forest (OARF). Uses the IPTW estimator to estimate the ATE while the propensity score is estimated via OARF. This leads to smaller variance and bias. Only variables that are confounders or predictive of the outcome are selected for the propensity score.

Positivity violations in marginal structural survival models with time-dependent confounding: a simulation study on IPTW-estimator performance.

Repository for "The Economic Consequences of UN Peacekeeping Operations: Causal Analysis for Conflict Management and Peace Research"

https://www.sciencedirect.com/science/article/pii/S001393512200305X

💬 Talk on "Sensitivity Analysis for Inverse Probability Weighting Estimators via the Percentile Bootstrap" (Q. Zhao et al., 2017), for S. Pimentel's "Observational Study Design and Causal Inference" seminar at Berkeley, Spring 2018