CausalLift: Python package for causality-based Uplift Modeling in real-world business

📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation

Bootstrapping for Propensity Score Analysis

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.

do the green drivers also drive longer? --- causal identification using the propensity score approach

counterfactual matching

Multinomial Propensity Score Trimming (Am J Epidemiol 2018)

TI Methods Speaker Series in collaboration with the Student and Recent Graduate Committee (SARGC) of the Statistical Society of Canada.

Propensity Score Analysis Graphics

implement machine learning models from scratch

How to use the Machine Learning Runtime and MLflow on top of a health Delta Lake to predict patient disease

Comparison of treatment effect in Randomized Control Trial (RCT) and Propensity Score Matching methods, conducted on Large-Scale Dataset by 'Criteo'.

POM-PS tests for genetic associations of secondary traits from case-control GWAS.

Propensity score assignment

Causal Inference - Does Critic Ratings affect Movie's Revenue

Replication of the paper "Voting Made Safe and Easy: The impact of e-voting on Citizen Perceptions," by Alvarez et. al and its extension using genetic matching.

my master's thesis project that studies the impact of UN MDG on US nonprofit industry spending

These are R implementations under lasso related priors for clustering structure with propensity score, principal stratification and outcomes in Bayesian inference

R package for propensity score weighting using machine learning methods

Quantitative Methods for Studying Elites: Demonstration for R