# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables

## Question
The task was to copy specified treatment-effect estimates, standard errors (SEs), confidence-interval (CI) bounds and sample counts exactly as printed in two sources: Armendariz (2026, arXiv:2609.27732v1, drug consumption rooms (DCRs), Tables 6–7) and Li, Pearson-Merkowitz & Yokum (2026, arXiv:2606.14621v1, Rhode Island RESEA, Table 2). Each value gets a locator. Two quantities that the sources do not print are derived from printed values, and any requested quantity that is not reported is identified. Nothing is re-estimated.

## Data and exclusions
- `data/sources/arxiv-2609.27732v1.txt` (134 lines), `data/sources/arxiv-2606.14621v1.txt` (58 lines) and `data/README.md` (10 lines) were read. No other evidence was used.
- I used only the tables named in each key's definition: DCR Tables 6 and 7, and RESEA Table 2. DCR Appendix Table A19 and RESEA Table 3 were used only for consistency checks.

## Method
`out/analysis.py` parses the pipe-separated table rows. It finds each row by its label and each column by its header, and treats the next non-empty line after a coefficient row as that row's SE line. It prints every source line it matched. Thousands separators are removed (e.g. "17,250" → 17250). Signs and decimals are kept as printed. One limitation: JSON numbers cannot keep trailing zeros, so 0.210 is stored as 0.21 and -0.340 as -0.34. The verbatim strings are therefore also kept in `results.json["printed_strings"]`. Significance is read from the stars in the table, using the table note: * means p<0.10, ** p<0.05, *** p<0.01. The script also checks that the RESEA Table 2 ATE column matches the Table 3 "Difference in means" column (it does, for all three outcomes).

## Results
Values are as printed, with locators in `results.json["locators"]`.

**DCR (log points; standard errors in parentheses)**
| key | value |
|---|---|
| Table 6 ATT, 5 km | -0.431** (0.210) |
| Table 6 ATT, 3 km | -0.283 (0.279) |
| Table 6 Observations, 5 km | 17,250 |
| Table 7 Narcotics, 3 km | 0.549 (0.187), printed with *** |
| Table 7 Drug-related (fatal), 1 km | -1.207 (0.137), printed with *** |
| Table 7 HIV, 5 km | -0.340 (0.294), no star → not significant at 10% |
| HIV 5 km exact p-value | **null — not reported** (the source prints only stars) |
| Derived 95% CI, Table 6 5 km ATT | [-0.8426, -0.0194] |

**RESEA (Table 2, "Average treatment effect estimate" column)**
| key | value |
|---|---|
| Wage ATE | $1,153, 95% CI [166, 2,141] (USD/yr) |
| SE printed for wage ATE? | No |
| Derived SE of wage ATE | 503.83 USD/yr |
| Control mean wage | $33,043 |
| Reemployment ATE | 1.47 pp |
| Weeks on UI ATE | -1.99 weeks, 95% CI [-2.24, -1.75] |

25 reported or derived items were assessed (`n_analysed`).

## Uncertainty
- **The DCR figures are standard errors.** The values in parentheses in Tables 6 and 7 are SEs clustered at the MedStat region level, according to the table notes. The paper prints no exact p-values or CIs for these cells; it marks significance with stars only. An exact p-value for HIV at 5 km is therefore reported as null. Computing one from -0.340/0.294 would need an extra distributional assumption, and the task asks for the printed p-value.
- **The RESEA figures are 95% confidence intervals** (together with two-sided p-values). The paper does not print SEs.
- **Derived DCR CI.** Computed as ATT ± 1.96 × SE: -0.431 ± 1.96 × 0.210, which gives [-0.8426, -0.0194]. This assumes the estimator is approximately normal. The paper's own figure CIs might use another critical value, such as one from a multiplier bootstrap. If the Appendix A19 SE of 0.218 were used instead, the CI would be [-0.858, -0.004].
- **Derived RESEA SE.** Computed as (2,141 − 166)/(2 × 1.96) = 503.83. This assumes the CI is a symmetric normal-theory (Wald) interval. The printed interval is nearly symmetric around 1,153 (987 below, 988 above), which fits that assumption. With N = 23,549, a t critical value would give essentially the same result. Because the printed bounds are rounded to whole dollars, the SE is uncertain by about ±0.5 USD.

## Limitations and open issues
Problems found in the sources (also listed in `data_issues`):
1. **DCR SE conflict.** The 5 km CS-DR ATT of -0.431 has SE 0.210 in Table 6 but 0.218 in Appendix Table A19, for the same specification with N = 17,250. I used Table 6, as the key definition requires.
2. **DCR control-region count.** Tables 6/7 and the A19 rows give 608 never-treated control regions, but the A19 note says 640.
3. **DCR abstract vs Table 7.** The abstract says fatal hospitalisations "fall by 35%". Table 7's 5 km fatal ATT of -0.354 log points corresponds to about −29.8%, and Section 6.1 itself says about 30%. The abstract appears to read log points as percent.
4. **DCR HIV wording.** The text calls the HIV estimates "close to zero", but at 5 km the estimate is -0.340 (SE 0.294). It is insignificant, but not small.
5. **RESEA weeks-on-UI CI.** The text prints the CI as magnitudes, [1.75, 2.24]. Table 2 prints [-2.24, -1.75], and I used Table 2's version.
6. **Rendering defect.** The RESEA extract contains "p ¡ 0.001" in the short-term weeks-on-UI paragraph. No key uses it.
7. **Re-set tables.** RESEA Tables 2–3 were re-set by the extract editor. I accepted them as verbatim, as the extract header states, and they agree with each other.

The task is complete. The only null value, the HIV p-value, is null because the source does not report it.