Add EDID validation tests against paper results

.gitignore

@@ -90,3 +90,6 @@ papers/
 
 # Local analysis notebooks (not committed)
 analysis/
+
+# Replication data (local only, not for distribution)
+replication_data/

tests/conftest.py

@@ -8,6 +8,10 @@
 import math
 import os
 import subprocess
+import sys
+
+# Make tests/helpers/ importable without adding all of tests/ to sys.path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "helpers"))
 
 # Force non-interactive matplotlib backend before any test imports it.
 # Prevents plt.show() from blocking the test suite on a GUI window.

tests/data/README.md

@@ -0,0 +1,55 @@
+# Test Data Fixtures
+
+## hrs_edid_validation.csv
+
+**Source:** Dobkin, C., Finkelstein, A., Kluender, R., & Notowidigdo, M. J. (2018).
+"The Economic Consequences of Hospital Admissions." *American Economic Review*, 108(2), 308-352.
+Replication kit: https://www.openicpsr.org/openicpsr/project/116186/version/V1/view
+
+**Sample selection:** Follows Sun & Abraham (2021), as used by Chen, Sant'Anna & Xie (2025)
+Section 6:
+
+1. Read `HRS_long.dta` from the Dobkin et al. replication kit
+2. Keep waves 7-11, retain only individuals present in all 5 waves
+3. Filter to ever-hospitalized individuals with `first_hosp >= 8`
+4. Filter to ages 50-59 at hospitalization (`age_hosp`)
+5. Drop wave 11 (no valid comparison group)
+6. Recode `first_hosp == 11` as never-treated (`inf`)
+
+**Expected counts:**
+
+| Column | Values |
+|--------|--------|
+| Total individuals | 656 |
+| Waves | 7, 8, 9, 10 |
+| Rows | 2,624 |
+| G=8 | 252 |
+| G=9 | 176 |
+| G=10 | 163 |
+| G=inf | 65 |
+
+**Columns:** `unit` (hhidpn), `time` (wave), `outcome` (oop_spend, 2005 dollars), `first_treat` (first_hosp)
+
+**Regeneration:** Requires the Dobkin et al. replication kit (`.gitignore`d as `replication_data/`).
+
+```python
+import pandas as pd, numpy as np
+df = pd.read_stata("replication_data/116186-V1/Replication-Kit/HRS/Data/HRS_long.dta")
+sub = df[df["wave"].isin([7, 8, 9, 10, 11])]
+balanced = sub.groupby("hhidpn")["wave"].nunique()
+sub = sub[sub["hhidpn"].isin(balanced[balanced == 5].index)]
+sub = sub[sub["hhidpn"].isin(sub[sub["first_hosp"].notna()]["hhidpn"].unique())]
+fh = sub.groupby("hhidpn")["first_hosp"].first()
+sub = sub[sub["hhidpn"].isin(fh[fh >= 8].index)]
+ages = sub.groupby("hhidpn")["age_hosp"].first()
+sub = sub[sub["hhidpn"].isin(ages[(ages >= 50) & (ages <= 59)].index)]
+sub = sub[sub["wave"] <= 10]
+sub["first_treat"] = sub["first_hosp"].apply(lambda x: np.inf if x == 11 else int(x))
+out = sub[["hhidpn", "wave", "oop_spend", "first_treat"]].copy()
+out.columns = ["unit", "time", "outcome", "first_treat"]
+out["unit"] = out["unit"].astype(int)
+out["time"] = out["time"].astype(int)
+out.sort_values(["unit", "time"]).reset_index(drop=True).to_csv(
+    "tests/data/hrs_edid_validation.csv", index=False
+)
+```