Detecting, Decomposing, and Stress-Testing Temporal Change in Repeated Decision Systems.
DecisionDrift
Detecting, Decomposing, and Stress-Testing Temporal Change in Repeated Decision Systems
Overview
DecisionDrift is an R package for auditing temporal drift in repeated binary decision systems — such as algorithmic screening tools, institutional review boards, or any AI system that issues repeated yes/no decisions over time.
It complements the decisionpaths package by shifting focus from path construction to system-level change:
| Package | Core question |
|---|---|
decisionpaths | What happened? Path construction, DRI, entropy, equity |
DecisionDrift | Did the system change? Drift detection, decomposition, stress-testing |
Installation
From GitHub (recommended)
# install.packages("remotes")
remotes::install_github("causalfragility-lab/DecisionDrift")
From CRAN (once accepted)
install.packages("DecisionDrift")
Quick start
library(DecisionDrift)
# --- 1. Build a drift panel object -----------------------------------------
dat <- data.frame(
id = rep(1:100, each = 8),
time = rep(1:8, times = 100),
decision = rbinom(800, 1, rep(seq(0.25, 0.55, length.out = 8), 100)),
group = rep(c("A", "B"), 50, each = 1)
)
dp <- dd_build(dat, id, time, decision,
group = group,
event_time = 5L) # known policy change at wave 5
# --- 2. Run the full audit in one call --------------------------------------
aud <- dd_audit(dp)
print(aud)
plot(aud)
# --- 3. Or call individual modules ------------------------------------------
dd_prevalence(dp) # Was the overall decision rate stable?
dd_transition(dp) # Did switching/persistence dynamics change?
dd_entropy_trend(dp) # Did path complexity evolve?
dd_group_drift(dp) # Did drift differ across groups?
dd_changepoint(dp) # Where did the system break?
# --- 4. Summary drift indices -----------------------------------------------
dd_indices(dp)
# DDI = Decision Drift Index
# TDI = Transition Drift Index
# GDD = Group Differential Drift
# CDB = Cumulative Drift Burden
# --- 5. Stress-test conclusions ---------------------------------------------
dd_robustness(dp) # Are conclusions stable across analytic choices?
dd_sensitivity(dp) # How vulnerable are conclusions to data problems?
Package architecture
DecisionDrift ├── dd_build() Core: build drift_panel object │ ├── Module 1: dd_prevalence() Drift in decision rate → DDI ├── Module 2: dd_transition() Drift in transition structure → TDI ├── Module 3: dd_entropy_trend() Drift in path complexity / stability ├── Module 4: dd_group_drift() Group-differential drift → GDD ├── Module 5: dd_changepoint() Change-point & regime detection │ ├── dd_indices() All four indices in one call (DDI, TDI, GDD, CDB) │ ├── dd_robustness() Stability across: balanced panel, LOPO, │ LOGO, min_waves grid, bootstrap CIs │ ├── dd_sensitivity() Vulnerability to: miscoding, missingness, │ threshold shifts, composition shifts │ └── dd_audit() Flagship: 3-layer audit → verdict
The four drift indices
| Index | Symbol | Meaning |
|---|---|---|
| Decision Drift Index | DDI | Standardised linear trend in decision rate. Positive = more permissive over time. |
| Transition Drift Index | TDI | Mean absolute change in transition probabilities. Higher = more volatile dynamics. |
| Group Differential Drift | GDD | Difference in trend slopes between groups. Non-zero = unequal drift. |
| Cumulative Drift Burden | CDB | Total accumulated wave-to-wave rate change (direction-agnostic). |
The three-layer audit
dd_audit() follows a deliberate three-layer logic:
Detection — Did the process drift?
dd_prevalence(),dd_transition()Decomposition — Was drift due to prevalence change, transition instability, subgroup divergence, or a regime shift?
dd_entropy_trend(),dd_group_drift(),dd_changepoint()Stress-testing — Are conclusions robust to noise, coding choices, and missing waves?
dd_robustness(),dd_sensitivity()
The audit closes with a single verdict: "no drift detected", "marginal drift", "moderate drift", or "strong drift".
Relationship to decisionpaths
decisionpaths answers: What paths occurred, how stable, how entropic, how equitable?
DecisionDrift answers: Did the system itself change, when, how much, for whom, and is that conclusion robust?
They are designed to be used together: build paths with decisionpaths::dp_build(), audit drift with DecisionDrift::dd_audit().
A third package in the ecosystem, AIBias, will ask: Did inequality accumulate or amplify over repeated decisions?
Dependencies
Imports:cli, rlang, stats, tibble
Suggests (for plotting):ggplot2, patchwork
Citation
Hait, S. (2026). DecisionDrift: Detecting, decomposing, and stress-testing temporal change in repeated decision systems. R package version 0.1.0. https://github.com/causalfragility-lab/DecisionDrift
License
MIT © Subir Hait.