Team project for BA830 (Business Experimentation and Causal Methods)
-
Updated
Jul 13, 2021
Team project for BA830 (Business Experimentation and Causal Methods)
Treatment-effects estimation by blop-matching in Stata
In this theoretical work we provide an unrestricted linear model for identifying treatment effects with time-varying qualification with panel data and repeated cross-sections. This model enables fixed and random effects estimation of treatment effects; the latter has not been considered before. In addition we identify new scenarios and respectiv…
📹 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.
Repository of causal inference models, with a shared syntax for users to swap between models. This repository also allows users to diagnose their causal estimates absent any ground truth measures.
Stata command abseff (immediate calculation of absolute effects from relative treatment estimates)
Estimating the Effect of Persuasion in Stata
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.
A biologically motivated mathematical formalism is used to estimate the relative risks of breast, lung and thyroid cancers in childhood cancer survivors due to concurrent therapy regimen. This model specifically includes possible organ-specific interaction between radiotherapy and chemotherapy. The model predicts relative risks for developing se…
Propensity Score based Matching via Distribution Learning
Multivariate Outcome Treatment Effect Random Forest
A package of wrapper functions that are useful when analyzing data from randomized controlled trials (RCTs, especially with three or more treatment assignments)
Replication files for Jun and Lee (2022)
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
Review: Data-driven methodology for detecting treatment effect heterogeneity
Accounting for hidden confounders in estimates of dose-response curves from observational data.
Causal Inference Econometrics Reading List and R Tutorials
Quantifying the Persistence of Misinformation: A Case Study in R
Add a description, image, and links to the treatment-effects topic page so that developers can more easily learn about it.
To associate your repository with the treatment-effects topic, visit your repo's landing page and select "manage topics."