Pinned Repositories
causal
Causal Inference using R. Examples are from the book Causal Inference (Chapman & Hall/CRC, 2012) by Miguel Hernán and Jamie Robins. Complete contributor/reviewer list is updated in http://www.hsph.harvard.edu/faculty/miguel-hernan/causal-inference-book/
EpiMethods
A unique open textbook to teach the nuances of applying advanced epidemiological methods using real data.
into2ML
A very basic introduction to machine learning.
intro2R
This is a R online textbook for those who are not familiar with data wrangling. For providing some practical introduction to data wrangling, NHANES datasets will be used as examples in this tutorial. Target audience is those interested in health data analysis, but these data wrangling skills are easily transferable to other fields. General understanding of a syntax based program is required as pre-requisite. For any comments regarding this document, reach out to Ehsan Karim http://ehsank.com/
iptw
This R package calculates various measures of association and helps understand and visualize the link between causal models such as Marginal Structural Models and Standerdized measures.
Scientific-Writing-for-Health-Research
Adaptation and Deployment of OER for Communicating Scientific Research Findings in Health Sciences Education
simMSM
R package that simulates data suitable for fitting Marginal Structural Model.
spph504-007
SPPH 504 (section 007): Application of Epidemiological Methods
SPPH504007SurveyData
Survey Data: Design and Examples
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.
ehsanx's Repositories
ehsanx/ehsanx
Github ReadMe
ehsanx/AIPW
R package: Augmented Inverse Probability Weighted (AIPW) estimation for average causal effect
ehsanx/autoencoderPS
ehsanx/clustMixType
:exclamation: This is a read-only mirror of the CRAN R package repository. clustMixType — k-Prototypes Clustering for Mixed Variable-Type Data
ehsanx/data
ehsanx/doubleml-for-r
DoubleML - Double Machine Learning in R
ehsanx/DSE2021_tutorials
ehsanx/dse_mk2021
ehsanx/free_r_tips
Free R-Tips is a FREE Newsletter provided by Business Science. It comes with bite-sized code tutorials every Tuesday.
ehsanx/grf
Generalized Random Forests
ehsanx/HDPS-Diagnostics
Code for graphical tools and sensitivity analyses when applying high-dimensional propensity score analyses
ehsanx/lmtp
:package: Non-parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies :crystal_ball:
ehsanx/MachineLearningLove
Code for simulation study conducted in "Machine Learning in Causal Inference: How do I love thee? Let me count the ways."
ehsanx/nestedcv
Nested cross-validation for accurate confidence intervals for prediction error.
ehsanx/PAPER-NN-Performance-Bootstrap-Tutorial
This repository contains material that has been linked in the MAKE paper on bootstrapping. No paper files are contained here. Only code.
ehsanx/parallel_demo
Demo on how to conduct parallel processing in R
ehsanx/precourse
A repo for the pre-course work at home exercises
ehsanx/pyprobml
Python code for "Machine learning: a probabilistic perspective" (2nd edition)
ehsanx/qgcomp
QGcomp (quantile g-computation): estimating the effects of exposure mixtures. Works for continuous, binary, and right-censored survival outcomes. Flexible, unconstrained, fast and guided by modern causal inference principles
ehsanx/rcf
heterogeneous treatment effect estimation with causal forests
ehsanx/SDRcausal
SDRcausal is a R Package that provides semiparametric estimators of Average Causal Effects, using sufficient dimension reduction for nuisance model estimation.
ehsanx/ser2021-workshop
Materials for the workshop "Targeted Learning in the tlverse: Causal Inference Meets Machine Learning" at the 2021 Society for Epidemiologic Research (SER) Meeting
ehsanx/ser2021_mediation_workshop
Materials for the workshop "Causal Mediation: Modern Methods for Path Analysis" at the 2021 Society for Epidemiologic Research Meeting
ehsanx/SimulationPerformance
ehsanx/stanford_dl_ex
Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial
ehsanx/tabnet
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
ehsanx/Thesis_Code
ehsanx/tidyAPM
tidymodels code for Applied Predictive Modeling
ehsanx/tlverse-handbook
🎯 :closed_book: Targeted Learning in R: Causal Data Science with the tlverse
ehsanx/txshift
:package: :game_die: R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Two-Phase Sampling Corrections