Must-read papers and resources related to causal inference and machine (deep) learning

PubMed 200k RCT dataset: a large dataset for sequential sentence classification.

Flowchart is a STATA module/package that generates publication-quality Subject Disposition Flowchart Diagrams in LaTeX Format. This package generates PGF/TikZ code through texdoc, compiled in LaTeX to produce the diagram as a PDF. The final diagram is the same in style as ones used in the PRISMA Statement, CONSORT 2010 Statement, or STROBE State…

CRAN Task View: Causal Inference

Web application to run meta-analyses

Analyze the effects of seasonal migration on household consumption and expenditures in rural Bangladesh from a randomized control trial.

Implementation for the paper "Efficient Randomized Experiments Using Foundation Models"

Implementing standard econometric models using Stochastic Gradient Descent and Perceptrons instead of MLE and GMM.

Code for "Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials"

API Client for the 'ClimMob' platform in R

Replication code for "Estimating population average treatment effects from experiments with noncompliance"

Shiny app for analysis of randomized clinical trials

Experiments showing the profit efficiency of targeted randomized sampling in comparison to standard A/B testing

Interactive Stepped-Wedge Cluster Randomised Trial Data Analysis Tool

Derive rejection boundaries for RCTs with inconsistent covariate adjustment

Estimation of covariate interactions and subgroup-specific treatment effects in meta-analysis.

Add Stata program for a textbook about randomized controlled trials (A/B testing)

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

Using R tools like ggplot2 and dplyr, analyzed small business data to understand uptake patterns for federal aid. Employed logistic regression and decision trees to pinpoint key predictors. Explored strategies like mailer campaigns to boost application rates and conducted power analysis for potential randomized trials.

Stroke2Work analyzes return-to-work and health outcomes in stroke survivors, using statistical models to identify patient subgroups most likely to benefit, optimize work reintegration timing, and segment individuals by projected recovery and long-term quality-of-life.