---
title: "Negative controls, placebo windows, and temporal leakage"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Negative controls, placebo windows, and temporal leakage}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

Predictive and process-feature workflows can accidentally use information that is unavailable at the intended decision boundary. The temporal provenance layer makes availability explicit.

```{r, eval=FALSE}
p <- process_feature_time_provenance(c("dwell_pre","rt_final"), c(400,1200), outcome_at=c(1000,1000))
audit_temporal_leakage(p)
```

Negative controls deliberately break a declared process–outcome relation and rerun the same analysis.

```{r, eval=FALSE}
nc <- run_process_negative_controls(data, outcome="y", analysis_fun=analysis_fun, replications=200)
summarise_process_negative_controls(nc)
process_null_benchmark(observed_effect, nc)
plot(nc)
```

A leakage flag denotes temporal/information contamination, not misconduct. Null-like negative controls are useful diagnostics but do not prove model validity.