econometrics-check

name: econometrics-checkdescription: Causal inference design audit for the sewage-house-prices project. Runs a 4-phase review (claim identification, design validity, inference, polish) covering hedonic pricing, repeat sales, long difference, DiD/event studies, upstream/downstream, and dry spill strategies. This skill should be used when asked to "check the econometrics", "audit the identification", "review the strategy", or when verifying that code matches the stated design.argument-hint: "[.tex section, .R script, directory path, or 'all']"allowed-tools: ["Read", "Grep", "Glob", "Write", "Agent"]

Econometrics Check

Run a 4-phase causal inference audit on the target file(s) for the sewage-house-prices project.

Input: $ARGUMENTS — a .tex file, .R script, directory path, or all.

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Project-Specific Context

Identification Strategies in This Paper

1. Hedonic regressions — Cross-sectional: log(price) on spill count/hours (continuous + bins), LSOA FE, heteroskedasticity-robust SE
2. Repeat sales — Within-property variation (Palmquist 1982), house fixed effects
3. Long difference — 250m grid-level changes, weighted/unweighted, all/exposed grids
4. News/media DiD — Google Trends and LexisNexis media coverage as treatment
5. Upstream/downstream — Directional spillover via PostGIS river network topology
6. Dry spills — Spills occurring without rainfall as identification variation (rainfall threshold-based)

Key Variables

• Outcome: log(price) for sales, log(rent) for rentals
• Treatment: spill_count, spill_hrs, spill_count_daily_avg, spill_hrs_daily_avg
• Geography: n_spill_sites within radius, min_dist / mean_dist
• Radii: 250m, 500m, 1000m, 2000m, 5000m, 10000m
• Fixed effects: LSOA (lsoa), MSOA (msoa), year-quarter
• SE: heteroskedasticity-robust via fixest::feols(vcov = "hetero")
• Counting: 12/24-hour methodology in spill_aggregation_utils.R

File Locations

• Manuscript: docs/overleaf/*.tex
• Analysis scripts: scripts/R/09_analysis/
• Utility functions: scripts/R/utils/
• Pipeline scripts: scripts/R/01_data_ingestion/ through scripts/R/06_analysis_datasets/

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Workflow

Step 1: Parse Input

Determine target from $ARGUMENTS:

• .tex file: Review manuscript section for identification claims, assumption statements, estimation descriptions
• .R file: Review analysis script for code-theory alignment, package usage, SE computation
• Directory (e.g. scripts/R/09_analysis/02_hedonic/): Review all scripts in that approach
• all: Review docs/overleaf/*.tex and scripts/R/09_analysis/
• No argument: Review all manuscript sections and analysis scripts

Step 2: Context Gathering

Before auditing:

1. Read the target file(s)
2. Read docs/overleaf/refs.bib for citation availability
3. If reviewing scripts: read corresponding manuscript section for code-theory alignment
4. If reviewing manuscript: read corresponding analysis scripts for accuracy
5. Read scripts/R/utils/spill_aggregation_utils.R for counting methodology

Step 3: Run 4-Phase Review

Phase 1: Claim Identification

• What design is being used? (hedonic / repeat sales / long diff / DiD / upstream-downstream / dry spill)
• What is the estimand? (ATT / ATE / LATE)
• What is the treatment? (spill count, spill hours, distance, media coverage)
• What is the comparison group?

Phase 2: Core Design Validity

Design-specific assumption checks:

Hedonic:

• Selection on observables assumption — are LSOA FE sufficient?
• Omitted variable bias from neighbourhood sorting
• Measurement error in spill count/hours

Repeat sales:

• Within-property variation correctly specified
• Time-varying confounders addressed
• Property-level fixed effects properly implemented

Long difference:

• Grid construction and assignment correct
• Weighted vs unweighted justified
• Pre-period balance

DiD / Event studies:

• Parallel trends assumption stated and tested
• Treatment timing correctly identified
• Staggered adoption issues addressed (if applicable)

Upstream/downstream:

• River network topology correctly implemented
• Directionality assumption valid
• Spillover definitions consistent

Dry spills:

• Rainfall threshold justified
• Dry vs wet spill definition consistent with data pipeline
• Selection into dry spills addressed

Phase 3: Inference

• SE computation: heteroskedasticity-robust applied correctly
• Multiple testing: across radii, specifications, outcomes
• Code-theory alignment: does the R code implement what the paper claims?

Phase 4: Polish & Completeness

• Robustness checks: radius sensitivity, time period variation, specification changes
• Sensitivity analysis: how sensitive are results to key assumptions?
• Citation fidelity: are methodological references accurate?

Early stopping: If Phase 2 finds CRITICAL issues, focus there.

Step 4: Present Summary

## Econometrics Audit: [target]
**Date:** YYYY-MM-DD

### Design(s) Reviewed
- [List designs audited]

### Overall Assessment: [SOUND / MINOR ISSUES / MAJOR ISSUES / CRITICAL ERRORS]

### Blocking Issues (CRITICAL)
1. ...

### Priority Action List
1. ...
2. ...
3. ...

### Positive Findings
- ...

Save report to output/log/econometrics_check_[target].md.

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Principles

• Design-opinionated, package-flexible. Validate the econometric logic, not just the R packages.
• Cross-reference code and paper. Flag any mismatch between what the manuscript says and what the script does.
• Actionable output. Every issue must have a concrete fix.
• Proportional. Not every approach needs every robustness check.
• Sequential phases. Never skip to robustness before verifying the core design holds.