5d2096fb5d
Implements a comprehensive suite of AI SDK v6 tools across multiple categories: - Research (5): page-brief, compare-pages, source-credibility, claim-checklist, timeline-from-text - Web (10): fetch-text, links-catalog, extract-meta, extract-json-ld, redirect-trace, sitemap-read, rss-read, table-extract, robots-policy, url-normalize - Data (15): csv-parse, csv-stringify, json-repair, json-schema-validate, yaml-parse, yaml-stringify, text-chunk, normalize-whitespace, dedupe-by-key, pivot, rows-filter, rows-sort, rows-group-aggregate, rows-join, schema-infer - Doc (12): toc-generate, glossary-build, faq-from-text, executive-brief, decision-record-adr, prd-outline, acceptance-criteria, style-rewrite - Eng (12): diff-text-unified, env-var-docs-generate, dependency-audit-lite, conventional-commit-suggest, markdown-lint-basic, test-case-generate, stacktrace-parse, release-notes, changelog-entry, release-checklist - Security (7): redact-secrets, secret-scan-text, url-risk-heuristic, csp-compose, hardening-checklist-web, access-control-matrix, data-classification-heuristic - Stats (9): effect-size-suite, bootstrap-ci, permutation-test, multiple-testing-adjust, linear-regression-ols, logistic-regression, time-series-decompose-lite, anomaly-detect-mad - Ops (7): slo-draft, runbook-draft, postmortem-draft, postmortem-action-extractor, error-log-triage, coverage-tracker, monitoring-gap-analysis - Agent (15): prompt-to-workflow-skeleton, workflow-validate-io, workflow-explain, workflow-cost-estimate, tool-call-accuracy-score, eval-fixture-build, guardrail-policy-draft, workflow-auto-repair, tool-selection-plan, novelty-score-workflow, workflow-variant-generate, config-normalize, recipe-* - Utility (8): base64-encode, base64-decode, hash-text, regex-extract, template-render, date-parse, json-path-query, url-parse - HTML (3): html-sanitize, html-to-markdown, markdown-to-html All tools follow AI SDK v6 pattern with tool() and jsonSchema<T>(). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> |
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Difference-in-Differences (DiD)
Causal inference estimator for measuring treatment effects using before/after comparison with a control group.
Installation
npm install @tpmjs/tools-diff-in-diff
Usage
import { diffInDiffTool } from '@tpmjs/tools-diff-in-diff';
// Example: Evaluate impact of a policy intervention
// Treatment group: Cities that implemented the policy
// Control group: Cities that did not implement the policy
const result = await diffInDiffTool.execute({
treatmentBefore: [100, 105, 98, 102], // Before policy
treatmentAfter: [120, 125, 118, 122], // After policy
controlBefore: [95, 100, 92, 98], // Before (no policy)
controlAfter: [98, 103, 95, 101], // After (no policy)
confidenceLevel: 0.95,
});
console.log(result);
// {
// effect: 17.5, // Treatment caused 17.5 unit increase
// standardError: 2.1,
// tStatistic: 8.33,
// pValue: 0.0001,
// significant: true,
// confidenceInterval: {
// lower: 13.2,
// upper: 21.8,
// level: 0.95
// },
// interpretation: "The treatment effect is 17.5 (increased by 17.5 units)...",
// groupMeans: {
// treatmentBefore: 101.25,
// treatmentAfter: 121.25,
// controlBefore: 96.25,
// controlAfter: 99.25
// },
// differences: {
// treatmentDiff: 20.0, // Treatment group change
// controlDiff: 3.0 // Control group change
// }
// }
API
Input
- treatmentBefore (required): Treatment group values before intervention
- treatmentAfter (required): Treatment group values after intervention
- controlBefore (required): Control group values before intervention
- controlAfter (required): Control group values after intervention
- confidenceLevel (optional): Confidence level (default: 0.95)
Output
- effect: Estimated causal treatment effect (DiD estimator)
- standardError: Standard error of the estimate
- tStatistic: Test statistic for significance testing
- pValue: Two-tailed p-value
- significant: Whether effect is statistically significant
- confidenceInterval: Confidence interval for the effect
- interpretation: Plain English interpretation
- groupMeans: Mean values for all four groups
- differences: Within-group changes over time
Algorithm
The DiD estimator removes time-invariant confounders by differencing:
Formula: DiD = (T_after - T_before) - (C_after - C_before)
Where:
- T = Treatment group
- C = Control group
This double-differencing removes:
- Time trends (via control group)
- Group differences (via before/after comparison)
Key Assumption: Parallel trends - Without treatment, both groups would have changed similarly.
Use Cases
- Policy evaluation: Measure impact of new regulations
- Marketing: Test effectiveness of campaigns
- Medicine: Clinical trials with before/after measurements
- Economics: Evaluate economic interventions
- Education: Assess program effectiveness
Example: Minimum Wage Study
// States that raised minimum wage (treatment)
// vs states that didn't (control)
const result = await diffInDiffTool.execute({
treatmentBefore: [5.2, 5.5, 5.1, 5.4], // Employment before
treatmentAfter: [5.1, 5.3, 5.0, 5.2], // Employment after
controlBefore: [5.3, 5.4, 5.2, 5.5],
controlAfter: [5.4, 5.5, 5.3, 5.6],
});
// Effect tells us the causal impact on employment
Interpretation
A positive effect means treatment increased the outcome. A negative effect means treatment decreased the outcome.
Statistical significance (p < 0.05) suggests the effect is real, not due to chance.
Limitations
- Requires parallel trends assumption
- Can't control for time-varying confounders
- Sensitive to outliers
- Needs sufficient sample size
License
MIT