Topic: inverse-probability-weights Goto Github
Some thing interesting about inverse-probability-weights
Some thing interesting about inverse-probability-weights
inverse-probability-weights,Taking Uncertainty Seriously: Bayesian Marginal Structural Models for Causal Inference in Political Science
User: ajnafa
inverse-probability-weights,Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data
User: andrewheiss
Home Page: https://www.andrewheiss.com/blog/2020/02/25/closing-backdoors-dags/
inverse-probability-weights,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.
User: awamaeva
inverse-probability-weights,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.
User: barleiris
inverse-probability-weights,Repository for "The Economic Consequences of UN Peacekeeping Operations: Causal Analysis for Conflict Management and Peace Research"
User: brian-lookabaugh
inverse-probability-weights,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.
User: ehsanx
Home Page: https://ehsanx.github.io/TMLEworkshop/
inverse-probability-weights,An implementation of g-methods
User: herdiantrisufriyana
inverse-probability-weights,Tools for using marginal structural models (MSMs) to answer causal questions in developmental science.
User: istallworthy
Home Page: https://istallworthy.github.io/devMSMs/
inverse-probability-weights,IPW- and CBPS-type propensity score reweighting, with various extensions (Stata package)
User: kkranker
Home Page: https://ideas.repec.org/c/boc/bocode/s458657.html
inverse-probability-weights,Positivity violations in marginal structural survival models with time-dependent confounding: a simulation study on IPTW-estimator performance.
User: mspreafico
Home Page: https://arxiv.org/abs/2403.19606
inverse-probability-weights,Code for assessing the causal effects of chemotherapy Received Dose Intensity (RDI) on survival outcomes in osteosarcoma patients using a Target Trial Emulation approach.
User: mspreafico
Home Page: https://arxiv.org/abs/2307.09405
inverse-probability-weights,R package for estimating balancing weights using optimization
User: ngreifer
Home Page: https://ngreifer.github.io/optweight/
inverse-probability-weights,WeightIt: an R package for propensity score weighting
User: ngreifer
Home Page: https://ngreifer.github.io/WeightIt/
inverse-probability-weights,:package: R/haldensify: Highly Adaptive Lasso Conditional Density Estimation
User: nhejazi
Home Page: https://codex.nimahejazi.org/haldensify
inverse-probability-weights,:package: R/medoutcon: Efficient Causal Mediation Analysis with Natural and Interventional Direct/Indirect Effects
User: nhejazi
Home Page: https://codex.nimahejazi.org/medoutcon
inverse-probability-weights,:package: :game_die: R/medshift: Causal Mediation Analysis for Stochastic Interventions
User: nhejazi
Home Page: https://codex.nimahejazi.org/medshift
inverse-probability-weights,: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
User: nhejazi
Home Page: https://statistics.berkeley.edu/~nhejazi/present/2018_ipwbootstrap_zhao.pdf
inverse-probability-weights,:speech_balloon: Talk on causal inference and variable importance with stochastic interventions under two-phase sampling
User: nhejazi
inverse-probability-weights,Epidemiology analysis package
User: pzivich
Home Page: http://zepid.readthedocs.org
inverse-probability-weights,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.
Organization: quantlet
inverse-probability-weights,https://www.sciencedirect.com/science/article/pii/S001393512200305X
User: shuxind
inverse-probability-weights,air pollution and mortality/readmission in ADRD population with Medicare data
User: shuxind
inverse-probability-weights,Inverse probability weighting for non-binary exposures. Simple example in Excel and SAS.
User: srhoffma
inverse-probability-weights,Optimization of KNN and Linear regression algorithms
User: sunidhit
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