# F4-conf-01 — Extracting treatment effects and their uncertainty from two published tables

# Question

The task is to pull specified treatment effects, standard errors (SEs), 95% confidence-interval (CI) bounds and counts out of two arXiv extracts, copying them exactly as printed. The sources are Armendariz (2026), arXiv:2609.27732v1, a staggered DiD study of Swiss drug consumption rooms (DCRs), using Tables 6 and 7; and Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, an RCT of Rhode Island's RESEA programme, using Table 2. Each value gets a locator. I also derive two quantities the sources do not print, state the convention used for each, and flag requested quantities that are not reported. Nothing is re-estimated.

# Data and exclusions

• Inputs: data/sources/arxiv-2609.27732v1.txt (134 lines), data/sources/arxiv-2606.14621v1.txt (58 lines) and data/README.md (10 lines) (n_input_rows). The SHA-256 of each extract matches the one listed in the README (out/state/data_audit.md).
• Nothing was filtered out. All 25 requested value items were found and assessed (n_analysed = 25; out/exclusions.csv shows 0 removed).
• Information rules: only the two extracts were used. Two other tables, DCR Table A19 and RESEA Table 3, also print some of the same numbers. I used them only for cross-checks, never as the source of a key.
• Problems found in the inputs (data_issues, 6 entries):
  1. Table 6 gives the 5 km CS-DR ATT (-0.431**) an SE of 0.210. Table A19 prints the same estimate with SE 0.218 (check_dcr_a19_se_5km). The key names Table 6, so I used Table 6.
  2. The number of control regions is inconsistent: 608 in Tables 6 and 7, but 640 in the Table A19 note. The printed observation counts are exactly 25 × (treated + 640) and are not whole multiples of (treated + 608). I report the observation count as printed.
  3. The DCR narrative contradicts itself. The abstract says fatal hospitalisations fall by 35%, while Section 6.1 says about 30% at 5 km. The text also calls the HIV estimates "close to zero", although the 5 km estimate is -0.340.
  4. The RESEA narrative gives the weeks-on-UI CI as unsigned magnitudes. I used the signed values from Table 2.
  5. There is a rendering defect, "p ¡ 0.001", in the RESEA text.
  6. The RESEA tables label the same numbers differently: "ATE" in Table 2 and ITT in Table 3. The extract does not describe how the CIs were built.

# Method

out/analysis.py parses the " | "-separated table lines and attaches each SE line in parentheses to the coefficient row above it. It removes $, thousands separators, stars and units, keeps the sign, and writes each printed literal into the JSON so that trailing zeros survive (for example 0.210 and -0.340). It also prints every source line it matched. Significance at the 10% level is read from the stars: any of *, ** or *** means p < 0.10.

The two derived quantities use the normal critical value 1.96 (derivation_notes):

• the DCR 95% CI is ATT ± 1.96 × SE;
• the RESEA wage SE is (upper − lower)/(2 × 1.96).

As a sensitivity check I repeated both with a critical value of 2.0 (baseline_* keys). The script involves no randomness (random_seed = null).

# Results

DCR study (arxiv-2609.27732v1), log points

• Table 6 reports 17250 observations for the 5 km buffer (dcr_obs_5km).
• The source does not mark the HIV 5 km ATT as significant at the 10% level (dcr_hiv_att_5km_significant_10pct = false).
• Its exact p-value is not printed, so it is reported as null (dcr_hiv_att_5km_p_value). The tables give significance stars only.
• Derived 95% CI for the 5 km ATT: [-0.8426, -0.0194] (dcr_att_5km_ci_low, dcr_att_5km_ci_high).

RESEA study (arxiv-2606.14621v1), Table 2

• Wage ATE: $1153 per year, 95% CI [166, 2141] (ri_wage_ate, ri_wage_ate_ci_low, ri_wage_ate_ci_high).
• No SE is printed for the wage ATE (ri_wage_ate_se_reported = false). The SE implied by the CI is 503.8265 (ri_wage_ate_se_derived).
• Control-group mean wage: $33043 (ri_control_mean_wage).
• Reemployment ATE: 1.47 pp (ri_reemployment_ate_pp).
• Weeks-on-UI ATE: -1.99 weeks, 95% CI [-2.24, -1.75] (ri_weeks_ui_ate, ri_weeks_ui_ate_ci_low, ri_weeks_ui_ate_ci_high).

Every locator is in locators. Feasibility is "complete" (feasibility).

# Uncertainty

• DCR values are standard errors. In Tables 6 and 7 the numbers in parentheses are SEs clustered at the MedStat region level. The stars mark p < 0.01, 0.05 and 0.10.

• RESEA values are confidence intervals and p-values. Table 2 prints two-sided p-values and 95% CIs, and no SEs.

• The derived DCR CI assumes normality. It assumes the ATT is approximately normally distributed with the printed SE, and uses a critical value of 1.96. It is consistent with the source: it excludes 0, and the implied normal p is 0.0401 (check_dcr_att_5km_p_implied_normal), which fits the printed **. With a critical value of 2.0 the CI would be [-0.851, -0.011] (baseline_dcr_att_5km_ci_low_z2, baseline_dcr_att_5km_ci_high_z2).

• The derived wage SE assumes a symmetric normal-approximation CI. It assumes the printed CI is estimate ± 1.96 × SE. Two checks support this:

  • the CI midpoint is 1153.5 (check_ri_wage_ci_midpoint);
  • the p-value implied by the derived SE is 0.0221 (check_ri_wage_p_implied_by_derived_se), close to the printed p = 0.022.

  With a critical value of 2.0 the SE would be 493.75 (baseline_ri_wage_ate_se_derived_z2). The extract does not describe how the CI was actually built. Because the CI bounds are rounded to whole dollars, the derived SE is only accurate to about ±0.3.

• The HIV 5 km estimate is not significant. Its |ATT/SE| is 1.1565 (check_dcr_hiv_5km_abs_z), below 1.645, which matches the absence of a star.

# Limitations and open issues

• The SE of the 5 km ATT is ambiguous. Tables 6 and A19 print different SEs (0.210 vs 0.218) for the same estimate. I followed the key's definition and used Table 6, but the source itself is inconsistent. With 0.218 the derived CI would be wider.
• The number of control regions is uncertain. The count is printed as 608, but 640 fits the observation counts. Which one is right is unresolved in the extract.
• The exact HIV p-value cannot be given. It is not reported, so it is null. Recovering it from the SE would require a distributional assumption, and the key asks only for a printed value.
• The derived quantities rest on convention. Both use the normal 1.96 critical value. A t critical value could not be used because the degrees of freedom are not reported (for DCR, the clusters are not stated consistently).
• The 3.5% wage figure was a check, not an extracted value. The ratio of the wage ATE to the control mean matches the narrative's 3.5% (out/state/checks.md, check 15).