propensity-score

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

📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation

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

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.

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.

implement machine learning models from scratch

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

Causal Inference - Does Critic Ratings affect Movie's Revenue

Propensity score assignment

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

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.

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

R code for the analyses conducted in Friedrich, S & Friede, T (2020). Causal inference methods for small non-randomized studies: Methods and an application in COVID-19. Submitted to Contemporary Clinical Trials.

R package for propensity score weighting using machine learning methods

Quantitative Methods for Studying Elites: Demonstration for R

Causal inference project