Tools for using marginal structural models (MSMs) to answer causal questions in developmental science.
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Updated
May 30, 2024 - R
Tools for using marginal structural models (MSMs) to answer causal questions in developmental science.
WeightIt: an R package for propensity score weighting
Positivity violations in marginal structural survival models with time-dependent confounding: a simulation study on IPTW-estimator performance.
Code for assessing the causal effects of chemotherapy Received Dose Intensity (RDI) on survival outcomes in osteosarcoma patients using a Target Trial Emulation approach.
📦 R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects
📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation
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.
air pollution and mortality/readmission in ADRD population with Medicare data
IPW- and CBPS-type propensity score reweighting, with various extensions (Stata package)
💬 Talk on causal inference and variable importance with stochastic interventions under two-phase sampling
📦 🎲 R/medshift: Causal Mediation Analysis for Stochastic Interventions
Epidemiology analysis package
Repository for "The Economic Consequences of UN Peacekeeping Operations: Causal Analysis for Conflict Management and Peace Research"
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
Taking Uncertainty Seriously: Bayesian Marginal Structural Models for Causal Inference in Political Science
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.
An implementation of g-methods
Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data
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