DPI: The Directed Prediction Index for Causal Direction Inference
from Observational Data
The Directed Prediction Index ('DPI') is a causal discovery method
for observational data designed to quantify the relative endogeneity
of outcome (Y) versus predictor (X) variables in regression models.
By comparing the coefficients of determination (R-squared)
between the Y-as-outcome and X-as-outcome models
while controlling for sufficient confounders and
simulating k random covariates, it can quantify relative endogeneity,
providing a necessary but insufficient condition for causal direction
from a less endogenous variable (X) to a more endogenous variable (Y).
Methodological details are provided at
<https://psychbruce.github.io/DPI/>.
This package also includes functions for data simulation and network
analysis (correlation, partial correlation, and Bayesian Networks).
| Version: |
2026.2 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
glue, crayon, cli, ggplot2, cowplot, qgraph, bnlearn, MASS |
| Suggests: |
bruceR, aplot, bayestestR |
| Published: |
2026-02-26 |
| DOI: |
10.32614/CRAN.package.DPI |
| Author: |
Han Wu Shuang Bao 
[aut, cre] |
| Maintainer: |
Han Wu Shuang Bao <baohws at foxmail.com> |
| BugReports: |
https://github.com/psychbruce/DPI/issues |
| License: |
GPL-3 |
| URL: |
https://psychbruce.github.io/DPI/ |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
DPI results |
Documentation:
Downloads:
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