DPI: The Directed Prediction Index for Quasi-Causal Inference with
Cross-Sectional Data
The Directed Prediction Index ('DPI') is
a quasi-causal inference method for cross-sectional data
designed to quantify the relative endogeneity (relative dependence)
of outcome (Y) versus predictor (X) variables in regression models.
By comparing the proportion of variance explained (R-squared)
between the Y-as-outcome model and the X-as-outcome model
while controlling for a sufficient number of possible confounders,
it suggests a plausible (admissible) direction of influence
from a more exogenous variable (X) to a more endogenous variable (Y).
Methodological details are provided at
<https://psychbruce.github.io/DPI/>.
| Version: |
2025.9 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
glue, crayon, cli, ggplot2, cowplot, qgraph, bnlearn, MASS |
| Suggests: |
bruceR, aplot |
| Published: |
2025-09-20 |
| 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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