This contains codes used for [Individual Differences in the Effects of Neighborhood Socioeconomic Deprivation on Economic Decision Making and Psychotic Risk in Children]. All analyses were conducted in R version 4.1.2.
Variable selection for Polygenic Risk Scores (PRS), Structural MRI, and MID Task fMRI using Boruta
Kursa, M. B., & Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11), 1 - 13. doi:10.18637/jss.v036.i11
Conventional Linear Instrumental Variable regression (IV regression)
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91(434), 444-455. doi:10.1080/01621459.1996.10476902
John Fox, Christian Kleiber, Achim Zeileis, Nikolas Kuschnig (2022). ivreg: Instrumental-Variables Regression by '2SLS', '2SM', or '2SMM', with Diagnostics. Version 0.6-1, URL: https://john-d-fox.github.io/ivreg/
Average Treatment Effects using Instrumental Random Forests (grf)
Wager, S., & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association, 113(523), 1228-1242. doi:10.1080/01621459.2017.1319839
Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. The Annals of Statistics, 47(2), 1148-1178. doi:10.1214/18-AOS1709
Heterogeneity assessment using Instrumental Random Forests (grf)
Chernozhukov, V., Demirer, M., Duflo, E. & Fernández-Val, I. Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India. National Bureau of Economic Research Working Paper Series No. 24678 (2018). DOI: 10.3386/w24678