Must-read papers and resources related to causal inference and machine (deep) learning
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Updated
Nov 23, 2022
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
API Client for the 'ClimMob' platform in R
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"
Replication code for "Estimating population average treatment effects from experiments with noncompliance"
Experiments showing the profit efficiency of targeted randomized sampling in comparison to standard A/B testing
Derive rejection boundaries for RCTs with inconsistent covariate adjustment
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'.
R Code and reporting output for randomized causal inference study to answer question on whether there is bias in following directions from gendered voices.
Interactive Stepped-Wedge Cluster Randomised Trial Data Analysis Tool
Second assignment for Artificial Intelligence course @USI19/20.
Standard Extensions for Variant Experiment Server
Estimation of covariate interactions and subgroup-specific treatment effects in meta-analysis.
Sketches Business Model Canvas, discusses A/B testing and randomized control trials (RCTs), codes a proof-of-concept in Python.
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.
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