diff --git a/changelog_entry.yaml b/changelog_entry.yaml index e69de29..500a9d3 100644 --- a/changelog_entry.yaml +++ b/changelog_entry.yaml @@ -0,0 +1,6 @@ +- bump: minor + changes: + added: + - Benchmarking experiments for wealth imputation paper draft. + - MDN model to experiments run in imputing-from-scf-to-cps.ipynb. + - Privacy & Terms to microimputation-dashboard. diff --git a/microimputation-dashboard/app/page.tsx b/microimputation-dashboard/app/page.tsx index a8bc1b6..8c731ae 100644 --- a/microimputation-dashboard/app/page.tsx +++ b/microimputation-dashboard/app/page.tsx @@ -8,12 +8,61 @@ import { parseImputationCSV } from '@/utils/csvParser'; import { ImputationDataPoint } from '@/types/imputation'; import { parseDeeplinkParams, GitHubArtifactInfo } from '@/utils/deeplinks'; +function PrivacyModal({ isOpen, onClose }: { isOpen: boolean; onClose: () => void }) { + if (!isOpen) return null; + + return ( +
+ All data uploaded to this dashboard is processed entirely within your browser. + No data is transmitted to or stored on PolicyEngine servers. When you close or + refresh this page, all loaded data is cleared from memory. +
++ This tool is provided "as is" without warranty of any kind, express or implied. + PolicyEngine assumes no responsibility for the security, accuracy, or confidentiality + of any data you choose to load into this application. +
++ Users are solely responsible for ensuring they have appropriate rights to use any + data loaded into this dashboard and for compliance with applicable data protection + regulations. +
+- Histogram with {(dist.data as BinData[]).length} bins. Each bin shows the percentage of values falling within that range. + Histogram with {(dist.data as BinData[]).length} bins. Drag the handles below to zoom into a specific range. Overlapping bars indicate similar distributions.
- Interpretation: Values closer to 0 are better. Generally, values below - 0.05 indicate good imputation quality, while values above 0.2 suggest significant - distributional differences. + Interpretation: Since Wasserstein distance is scale-dependent, quality is assessed + relative to each variable's range. A distance of <1% of the variable range is excellent, + <3% is good, <5% is moderate, <10% is fair, and ≥10% suggests poor distributional match.
@@ -130,14 +171,21 @@ export default function ImputationResults({ data }: ImputationResultsProps) {| \n", - " | is_female | \n", - "age | \n", - "race | \n", - "employment_income | \n", - "interest_dividend_income | \n", - "pension_income | \n", - "
|---|---|---|---|---|---|---|
| is_female | \n", - "1.000000 | \n", - "-0.018881 | \n", - "0.057994 | \n", - "-0.085917 | \n", - "-0.037146 | \n", - "-0.055694 | \n", - "
| age | \n", - "-0.018881 | \n", - "1.000000 | \n", - "-0.204776 | \n", - "0.018204 | \n", - "0.093807 | \n", - "0.283672 | \n", - "
| race | \n", - "0.057994 | \n", - "-0.204776 | \n", - "1.000000 | \n", - "-0.046325 | \n", - "-0.036117 | \n", - "-0.111804 | \n", - "
| employment_income | \n", - "-0.085917 | \n", - "0.018204 | \n", - "-0.046325 | \n", - "1.000000 | \n", - "0.056797 | \n", - "0.040700 | \n", - "
| interest_dividend_income | \n", - "-0.037146 | \n", - "0.093807 | \n", - "-0.036117 | \n", - "0.056797 | \n", - "1.000000 | \n", - "0.121772 | \n", - "
| pension_income | \n", - "-0.055694 | \n", - "0.283672 | \n", - "-0.111804 | \n", - "0.040700 | \n", - "0.121772 | \n", - "1.000000 | \n", - "
| \n", - " | networth | \n", - "
|---|---|
| is_female | \n", - "0.014095 | \n", - "
| age | \n", - "0.119553 | \n", - "
| race | \n", - "0.027996 | \n", - "
| employment_income | \n", - "0.121915 | \n", - "
| interest_dividend_income | \n", - "0.085286 | \n", - "
| pension_income | \n", - "0.059902 | \n", - "
| \n", - " | predictor_removed | \n", - "avg_quantile_loss | \n", - "avg_log_loss | \n", - "loss_increase | \n", - "relative_impact | \n", - "baseline_quantile_loss | \n", - "baseline_log_loss | \n", - "
|---|---|---|---|---|---|---|---|
| 4 | \n", - "interest_dividend_income | \n", - "3.413499e+06 | \n", - "0 | \n", - "1.865978e+06 | \n", - "120.578572 | \n", - "1.547521e+06 | \n", - "0 | \n", - "
| 3 | \n", - "employment_income | \n", - "2.073023e+06 | \n", - "0 | \n", - "5.255021e+05 | \n", - "33.957678 | \n", - "1.547521e+06 | \n", - "0 | \n", - "
| 1 | \n", - "age | \n", - "2.051171e+06 | \n", - "0 | \n", - "5.036499e+05 | \n", - "32.545597 | \n", - "1.547521e+06 | \n", - "0 | \n", - "
| 5 | \n", - "pension_income | \n", - "1.724660e+06 | \n", - "0 | \n", - "1.771395e+05 | \n", - "11.446667 | \n", - "1.547521e+06 | \n", - "0 | \n", - "
| 2 | \n", - "race | \n", - "1.586420e+06 | \n", - "0 | \n", - "3.889930e+04 | \n", - "2.513653 | \n", - "1.547521e+06 | \n", - "0 | \n", - "
| 0 | \n", - "is_female | \n", - "1.579172e+06 | \n", - "0 | \n", - "3.165105e+04 | \n", - "2.045275 | \n", - "1.547521e+06 | \n", - "0 | \n", - "