Review: Data-driven methodology for detecting treatment effect heterogeneity
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Updated
Feb 26, 2021 - R
Review: Data-driven methodology for detecting treatment effect heterogeneity
📹 Análise de dados sobre psicoeducação em vídeo do projeto TelePsi.
Github for the final project in Econometrics. Replication of Cameron, Gelbach and Miller (2008) and extension to "skewed" treatment variable.
Comparison of treatment effect in Randomized Control Trial (RCT) and Propensity Score Matching methods, conducted on Large-Scale Dataset by 'Criteo'.
R code (Rmd and Rnw) of the analysis on treatment effect of income on religiousness (Busser 2015)
Find sub groups (segments) with heterogeneous treatment effect in Randomised Controlled Trial data.
Propensity Score based Matching via Distribution Learning
Bounding Treatment Effects by Pooling Limited Information across Observations
🎯 🔀 Targeted Learning for Causal Mediation Analysis
Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
A package of wrapper functions that are useful when analyzing data from randomized controlled trials (RCTs, especially with three or more treatment assignments)
Univariate conditional average treatment effect estimation for predictive biomarker discovery
Gaines and Kuklinski (2011) Estimators for Hybrid Experiments
Adaptive debiased machine learning of treatment effects with the highly adaptive lasso
Replication of CPS data analysis and numerical experiments in the article "Confounder importance learning for treatment effect inference"
Tidy methods for Bayesian treatment effect models
🎯 🎲 Targeted Learning of the Causal Effects of Stochastic Interventions
Causal Inference in Case-Control Studies
Deep Treatment Learning (R)
Endogenous switching regression model
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