Epidemiology analysis package

• UpdatedMay 7, 2023
• Python

WeightIt: an R package for propensity score weighting

• UpdatedJul 10, 2025
• R

An R package for modern methods for non-probability samples

• UpdatedMay 24, 2025
• R

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

• UpdatedSep 15, 2022
• HTML

📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation

• UpdatedFeb 5, 2025
• R

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

• UpdatedFeb 27, 2020

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

• UpdatedJul 7, 2025
• R

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.

• UpdatedJan 5, 2023
• HTML

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

• UpdatedMay 19, 2023
• R

R package for estimating balancing weights using optimization

• UpdatedOct 28, 2022
• R

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

• UpdatedOct 10, 2024
• R

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.

• UpdatedJun 16, 2025
• R

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

• UpdatedSep 29, 2023
• Stata

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

• UpdatedApr 11, 2019
• Rich Text Format

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

• UpdatedJan 23, 2025
• R

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.

• UpdatedJan 27, 2025
• R

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

• UpdatedJun 26, 2024
• TeX

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

• UpdatedJan 10, 2025
• R

A questionnaire containing 40+ questions is given to hundreds of people. People are interviewed about their feelings and hobbies with a goal to find the causal relationship between depression and cognitive impairment, where some questions are related to depression, some to cognitive impairment, and others are confounding. In psychological survey…

• UpdatedMar 16, 2019

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

• UpdatedApr 25, 2023
• R