Shunkei Kakimoto's repositories
VRA_with_CF
This repository has the R and Python codes and data related to manuscript "Causal Forest Approach for Site-specific Input Management via On-farm Precision Experimentation." by Kakimoto, S., Mieno, T., Tanaka, T.S.T., & Bullock, D.S.
Applied_Econometrics_TA
Materials for TA sesseions
census-regions
US Census Bureau Regions and Divisions by State
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
final_assignment
This is a repository for the final project of APEC8601 in 2024 Spring
R-as-GIS-for-Economists
This repository has codes for the R as GIS for Economists book including the Rmd files for the book chapters.
Shunkei3.github.io
Personal website
Table-with-flextable
Example codes to create publication-quality tables with flexible
XBCF
R and python implementations of Accelerated Bayesian Causal Forest.