The Directed Prediction Index for Causal Direction Inference from Observational Data
Directed acyclic graphs (DAGs) via Bayesian networks (BNs).
Produce a symmetric correlation matrix from values.
Correlation and partial correlation networks.
DPI curve analysis across multiple random covariates.
Directed acyclic graphs (DAGs) via DPI exploratory analysis (causal di...
[S3 methods] for cor_net(), BNs_dag(), and DPI_dag().
Simulate experiment-like data with independent binary Xs.
Simulate data from a multivariate normal distribution.
DPI: The Directed Prediction Index for Causal Direction Inference from...
The Directed Prediction Index (DPI).
Convert p values to approximate (pseudo) Bayes Factors (PseudoBF10...
[S3 methods] for DPI() and DPI_curve().
The Directed Prediction Index ('DPI') is a quasi-causal inference (causal discovery) method for observational 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 can suggest a plausible (admissible) direction of influence 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).