inverse-probability-weights
There are 0 repository under inverse-probability-weights topic.
- zEpidLanguage:Python 132
- Language:R 97
ajnafa / Latent-Bayesian-MSM
Taking Uncertainty Seriously: Bayesian Marginal Structural Models for Causal Inference in Political Science
Language:HTML 17nhejazi / haldensify
:package: R/haldensify: Highly Adaptive Lasso Conditional Density Estimation
Language:R 15andrewheiss / closing-backdoors-dags
Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data
:package: R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects
Language:R 11:package: :game_die: R/medshift: Causal Mediation Analysis for Stochastic Interventions
Language:R 9ehsanx / TMLEworkshop
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.
Language:HTML 8- Language:R 8
kkranker / psweight
IPW- and CBPS-type propensity score reweighting, with various extensions (Stata package)
Language:Stata 4istallworthy / devMSMs
Tools for using marginal structural models (MSMs) to answer causal questions in developmental science.
Language:R 3awamaeva / R-package-trajmsm
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.
Language:R 2:speech_balloon: Talk on causal inference and variable importance with stochastic interventions under two-phase sampling
Language:TeX 1QuantLet / Outcome-adaptive-Random-Forest
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.
Language:R 1srhoffma / non_binary_exposure_IPW
Inverse probability weighting for non-binary exposures. Simple example in Excel and SAS.
Language:Rich Text Format 1Barleiris / Simple-Questionnaire-Analysis
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 surveys data are often ordinal, containing missing values, This repository provides a few approaches of analyzing the correlation among multiple frames of survey data using R, including redundancy analysis, Inverse Propensity Score Weighting, and Conditioning Copula, which is a method I invented.
0Brian-Lookabaugh / UN-PKOs-Project
Repository for "The Economic Consequences of UN Peacekeeping Operations: Causal Analysis for Conflict Management and Peace Research"
Language:R 0herdiantrisufriyana / gmethods
An implementation of g-methods
Language:R 0mspreafico / TTEcausalRDI
Code for assessing the causal effects of chemotherapy Received Dose Intensity (RDI) on survival outcomes in osteosarcoma patients using a Target Trial Emulation approach.
Language:R 0ShuxinD / causal_birthweight
https://www.sciencedirect.com/science/article/pii/S001393512200305X
Language:R 0air pollution and mortality/readmission in ADRD population with Medicare data
Language:R 0- Language:Jupyter Notebook 0
Positivity violations in marginal structural survival models with time-dependent confounding: a simulation study on IPTW-estimator performance.
Language:R:speech_balloon: 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
Language:TeX
