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| <html lang="en"> |
| <head> |
| <meta charset="UTF-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> |
| <title>Exactly Computing do-Shapley Values — ICML 2026 Reproduction</title> |
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| <script> |
| window.MathJax = { |
| tex: { |
| inlineMath: [['$', '$'], ['\\(', '\\)']], |
| displayMath: [['$$', '$$'], ['\\[', '\\]']], |
| packages: {'[+]': ['ams']} |
| }, |
| svg: { fontCache: 'global' } |
| }; |
| </script> |
| <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js"></script> |
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| <style> |
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| @page { size: 24in 45in; margin: 0; } |
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| :root { |
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| --accent: #24689B; |
| --accent-deep: #17496F; |
| --accent-light: #E7F1F8; |
| --accent-soft: #D4E7F3; |
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| --gold: #B44738; |
| --gold-soft: #FBEDEB; |
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| --text-primary: #1A1A1A; |
| --text-secondary: #555555; |
| --text-muted: #888888; |
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| --bg-page: #F6F2F0; |
| --bg-card: #FFFFFF; |
| --bg-card-tint: #FAFAFB; |
| --bg-emphasis: var(--accent-light); |
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| --border-soft: #D8D8D8; |
| --border-strong: var(--accent); |
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| --bg-screen: #2b2b2b; |
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| --u: 1.6px; |
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| --fs-1: calc(9 * var(--u)); |
| --fs-2: calc(10 * var(--u)); |
| --fs-3: calc(11 * var(--u)); |
| --fs-4: calc(12 * var(--u)); |
| --fs-5: calc(13 * var(--u)); |
| --fs-6: calc(15 * var(--u)); |
| --fs-7: calc(16 * var(--u)); |
| --fs-8: calc(22 * var(--u)); |
| --fs-9: calc(32 * var(--u)); |
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| --font-serif: "Charter", "Source Serif Pro", "Georgia", serif; |
| --font-sans: "Inter", "Helvetica Neue", sans-serif; |
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| --shadow-screen: 0 0 60px rgba(0, 0, 0, 0.5); |
| --shadow-card: 0 calc(2 * var(--u)) calc(6 * var(--u)) rgba(45, 95, 139, 0.05); |
| --ornament-ink: rgba(45, 95, 139, 0.06); |
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| } |
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| * { box-sizing: border-box; margin: 0; padding: 0; } |
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| html, body { |
| background: var(--bg-screen); |
| font-family: var(--font-serif); |
| color: var(--text-primary); |
| -webkit-font-smoothing: antialiased; |
| } |
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| .poster { |
| width: calc(610 * var(--u)); |
| height: calc(1143 * var(--u)); |
| background: var(--bg-page); |
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| background-image: |
| radial-gradient(ellipse at top left, rgba(45, 95, 139, 0.06), transparent 40%), |
| radial-gradient(ellipse at bottom right, rgba(201, 162, 74, 0.05), transparent 50%); |
| margin: 20px auto; |
| padding: calc(10 * var(--u)) calc(12 * var(--u)); |
| display: grid; |
| |
| grid-template-rows: auto 1fr auto; |
| gap: calc(6 * var(--u)); |
| box-shadow: var(--shadow-screen); |
| position: relative; |
| overflow: hidden; |
| } |
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| .poster::before { |
| content: ""; |
| position: absolute; top: 0; left: 0; right: 0; |
| height: calc(8 * var(--u)); |
| background: var(--accent); |
| } |
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| .header { |
| display: grid; |
| grid-template-columns: 1fr minmax(50%, auto) 1fr; |
| align-items: center; |
| gap: calc(14 * var(--u)); |
| padding: calc(1 * var(--u)) calc(3 * var(--u)) calc(4 * var(--u)); |
| border-bottom: calc(2 * var(--u)) solid var(--accent); |
| } |
| |
| .venue-badge { |
| justify-self: start; |
| display: flex; flex-direction: column; |
| align-items: center; justify-content: center; |
| min-width: calc(44 * var(--u)); |
| text-align: center; |
| border-right: calc(1 * var(--u)) solid var(--border-soft); |
| padding-right: calc(4 * var(--u)); |
| } |
| |
| .venue-badge .vb-venue { |
| font-family: var(--font-sans); |
| font-weight: 800; |
| font-size: var(--fs-9); |
| color: var(--accent-deep); |
| line-height: 1; |
| letter-spacing: -0.5px; |
| } |
| .venue-badge .vb-year { |
| font-family: var(--font-sans); |
| font-size: var(--fs-4); |
| color: var(--text-secondary); |
| margin-top: calc(3 * var(--u)); |
| letter-spacing: 1.2px; |
| } |
| .venue-badge .vb-tag { |
| font-family: var(--font-sans); |
| font-size: var(--fs-2); |
| color: var(--accent); |
| font-weight: 700; |
| margin-top: calc(2 * var(--u)); |
| letter-spacing: 1.2px; |
| } |
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| .title-block { text-align: center; min-width: 0; } |
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| .title { |
| font-family: var(--font-sans); |
| font-weight: 800; |
| font-size: var(--fs-9); |
| line-height: 1.05; |
| color: var(--accent-deep); |
| letter-spacing: -0.5px; |
| } |
| .title .accent { color: var(--gold); } |
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| .subtitle { |
| font-family: var(--font-sans); |
| font-weight: 500; |
| font-size: var(--fs-6); |
| color: var(--text-secondary); |
| margin-top: calc(2 * var(--u)); |
| font-style: italic; |
| } |
| .authors-line { |
| font-family: var(--font-sans); |
| font-size: var(--fs-5); |
| color: var(--accent); |
| font-weight: 600; |
| margin-top: calc(3 * var(--u)); |
| } |
| .authors-line .author { margin: 0 calc(4 * var(--u)); } |
| .authors-line sup { font-size: 0.7em; color: var(--accent); } |
| .authors-line .aff { |
| color: var(--text-secondary); |
| font-weight: 400; |
| display: block; |
| margin-top: calc(2 * var(--u)); |
| font-size: var(--fs-3); |
| } |
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| .right-block { |
| justify-self: end; |
| display: flex; align-items: center; |
| gap: calc(8 * var(--u)); |
| } |
| .qr-block { display: flex; flex-direction: column; align-items: center; gap: calc(2 * var(--u)); } |
| .qr-block img { |
| width: calc(75 * var(--u)); |
| height: calc(75 * var(--u)); |
| border: calc(2 * var(--u)) solid var(--accent); |
| border-radius: calc(4 * var(--u)); |
| background: white; |
| padding: calc(2 * var(--u)); |
| } |
| .qr-label { |
| font-family: var(--font-sans); |
| font-size: var(--fs-2); |
| color: var(--accent); |
| font-weight: 600; |
| } |
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| .logo-slot img { height: calc(75 * var(--u)); width: auto; max-width: calc(300 * var(--u)); object-fit: contain; display: block; } |
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| .logo-slot.logo-tall img, |
| .logo-slot.logo-square img { height: calc(75 * var(--u)); } |
| .logo-slot.logo-wide img { height: calc(51 * var(--u)); max-width: calc(125 * var(--u)); } |
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| .logo-chip { |
| display: inline-flex; align-items: center; justify-content: center; |
| background: var(--bg-card); border-radius: calc(3 * var(--u)); |
| padding: calc(3 * var(--u)) calc(4 * var(--u)); |
| } |
| .logo-chip.logo-chip-dark { background: var(--text-primary); } |
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| .logo-row { display: flex; align-items: center; gap: calc(4 * var(--u)); } |
| .logo-row img { height: calc(58 * var(--u)); width: auto; display: block; } |
| .logo-row .lr-item { |
| display: flex; flex-direction: column; align-items: center; |
| gap: calc(2 * var(--u)); |
| background: var(--bg-card); |
| border: 1px solid var(--border-soft); |
| border-radius: calc(3 * var(--u)); |
| padding: calc(3 * var(--u)) calc(5 * var(--u)); |
| } |
| .logo-row .lr-item img { height: calc(48 * var(--u)); } |
| .logo-row .lr-label { |
| font-family: var(--font-sans); font-weight: 600; font-size: var(--fs-1); |
| color: var(--text-secondary); text-align: center; line-height: 1.15; |
| } |
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| .logo-row.logo-stack { flex-direction: column; align-items: flex-start; gap: calc(6 * var(--u)); } |
| .logo-row.logo-stack img { width: calc(118 * var(--u)); height: auto; } |
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| .right-block:has(.logo-stack) { flex-direction: column; align-items: flex-end; } |
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| .venue-badge img { height: calc(46 * var(--u)); width: auto; display: block; margin: 0 auto calc(2 * var(--u)); } |
| .venue-badge .vb-title { font-family: var(--font-sans); font-weight: 800; font-size: var(--fs-5); color: var(--accent-deep); letter-spacing: 0.5px; } |
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| .body-grid { |
| display: grid; |
| grid-template-columns: 1fr 1fr; |
| gap: calc(12 * var(--u)); |
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| min-height: 0; |
| } |
| .column { |
| display: flex; flex-direction: column; |
| gap: calc(5 * var(--u)); |
| min-height: 0; |
| height: 100%; |
| padding-bottom: calc(4 * var(--u)); |
| } |
| .column:first-child .card { flex: 1; } |
| .column:last-child { gap: calc(3.5 * var(--u)); } |
| .column:first-child .card { padding: calc(3 * var(--u)) calc(7 * var(--u)); } |
| .column:first-child .body-text, |
| .column:first-child .card p, |
| .column:first-child .card li { font-size: var(--fs-3); } |
| .column:first-child .eqn { font-size: var(--fs-3); padding: calc(2 * var(--u)) calc(7 * var(--u)); margin: calc(2 * var(--u)) 0; } |
| .column:first-child .result-table { font-size: var(--fs-3); } |
| .column:first-child .result-table th, |
| .column:first-child .result-table td { padding: calc(1 * var(--u)) calc(3 * var(--u)); } |
| .column:first-child .callout { font-size: var(--fs-4); padding: calc(3 * var(--u)) calc(8 * var(--u)); margin: calc(3 * var(--u)) 0; } |
| .column:last-child .card:last-child { padding-bottom: calc(12 * var(--u)); } |
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| .card { |
| background: var(--bg-card); |
| border-radius: calc(5 * var(--u)); |
| padding: calc(4 * var(--u)) calc(8 * var(--u)); |
| border: calc(1 * var(--u)) solid var(--border-soft); |
| box-shadow: var(--shadow-card); |
| position: relative; |
| } |
| .card.tinted { background: var(--bg-card-tint); } |
| .card.card--compact { padding: calc(3 * var(--u)) calc(7 * var(--u)); } |
| |
| .card.highlight { |
| border-left: calc(6 * var(--u)) solid var(--accent); |
| background: var(--bg-emphasis); |
| } |
| |
| |
| .section-title { |
| font-family: var(--font-sans); |
| font-weight: 700; |
| font-size: var(--fs-6); |
| color: var(--accent-deep); |
| margin-bottom: calc(3 * var(--u)); |
| display: flex; align-items: center; |
| gap: calc(5 * var(--u)); |
| } |
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| .section-title .st-text { flex: 1; min-width: 0; line-height: 1.18; } |
| |
| |
| .section-title:not(:has(.st-text)) { display: block; line-height: 1.18; } |
| .section-title:not(:has(.st-text)) .num { float: left; margin-right: calc(5 * var(--u)); } |
| .section-title .num { |
| display: inline-flex; align-items: center; justify-content: center; |
| width: calc(24 * var(--u)); height: calc(24 * var(--u)); |
| background: var(--accent); color: white; |
| border-radius: 50%; |
| font-size: var(--fs-6); font-weight: 700; |
| flex-shrink: 0; |
| } |
| |
| .section-title .tag-key { |
| color: var(--gold); |
| font-size: var(--fs-4); |
| } |
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| |
| .body-text, .card p, .card li { |
| font-family: var(--font-serif); |
| font-size: var(--fs-4); |
| line-height: 1.35; |
| color: var(--text-primary); |
| } |
| .card ul, .card ol { padding-left: calc(20 * var(--u)); } |
| .card li { margin-bottom: calc(2 * var(--u)); } |
| |
| .keyword { color: var(--accent); font-weight: 700; } |
| .keyword-gold { color: var(--gold); font-weight: 700; } |
| .highlight-text { |
| background: var(--bg-emphasis); |
| padding: 0 calc(3 * var(--u)); |
| border-radius: calc(2 * var(--u)); |
| } |
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| |
| .eqn { |
| background: var(--bg-emphasis); |
| border-left: calc(3 * var(--u)) solid var(--accent); |
| padding: calc(4 * var(--u)) calc(10 * var(--u)); |
| margin: calc(4 * var(--u)) 0; |
| font-size: var(--fs-4); |
| overflow-x: hidden; |
| } |
| |
| |
| |
| .eqn--large { font-size: calc(var(--fs-5) * 1.25); } |
| .eqn .label { |
| display: block; |
| font-family: var(--font-sans); |
| font-size: var(--fs-2); |
| color: var(--accent); |
| font-weight: 600; |
| margin-bottom: calc(2 * var(--u)); |
| text-transform: uppercase; |
| letter-spacing: 1px; |
| } |
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| |
| .callout { |
| background: var(--accent); |
| color: white; |
| padding: calc(5 * var(--u)) calc(10 * var(--u)); |
| border-radius: calc(4 * var(--u)); |
| font-size: var(--fs-5); |
| margin: calc(4 * var(--u)) 0; |
| } |
| .callout strong { color: white; } |
| .callout.gold { |
| background: var(--gold-soft); |
| border-left: calc(6 * var(--u)) solid var(--gold); |
| color: var(--accent-deep); |
| } |
| .callout.gold strong { color: var(--accent-deep); } |
| .callout.callout--tight { padding-top: calc(4 * var(--u)); padding-bottom: calc(4 * var(--u)); margin-top: calc(3 * var(--u)); margin-bottom: calc(2 * var(--u)); } |
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| |
| .figure { margin: calc(4 * var(--u)) 0; text-align: center; } |
| .figure img:not([class*="w-"]) { width: 100%; } |
| .figure--wide img { width: 100%; } |
| .figure img { |
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| border-radius: calc(4 * var(--u)); |
| border: calc(1 * var(--u)) solid var(--border-soft); |
| background: white; |
| } |
| .figure .caption { |
| font-family: var(--font-sans); |
| font-size: var(--fs-3); |
| color: var(--text-secondary); |
| margin-top: calc(3 * var(--u)); |
| line-height: 1.3; |
| text-align: left; |
| } |
| .figure .caption strong { color: var(--accent-deep); } |
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| .fig-wrap::after { content: ""; display: table; clear: both; } |
| .ff-fig { |
| float: right; |
| width: 48%; max-width: 58%; min-width: 38%; |
| margin: calc(1 * var(--u)) 0 calc(3 * var(--u)) calc(11 * var(--u)); |
| text-align: center; |
| } |
| .ff-fig.left { |
| float: left; |
| margin: calc(1 * var(--u)) calc(11 * var(--u)) calc(3 * var(--u)) 0; |
| } |
| .ff-fig img { |
| display: block; |
| width: 100%; |
| border-radius: calc(4 * var(--u)); |
| border: calc(1 * var(--u)) solid var(--border-soft); |
| background: white; |
| } |
| .ff-fig .caption { |
| font-family: var(--font-sans); |
| font-size: var(--fs-3); |
| color: var(--text-secondary); |
| margin-top: calc(3 * var(--u)); |
| line-height: 1.3; |
| text-align: center; |
| } |
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| .result-table { |
| width: 100%; |
| border-collapse: collapse; |
| font-family: var(--font-sans); |
| font-size: var(--fs-4); |
| margin-top: calc(3 * var(--u)); |
| } |
| .result-table th, .result-table td { |
| padding: calc(2 * var(--u)) calc(4 * var(--u)); |
| text-align: center; |
| border-bottom: calc(1 * var(--u)) solid var(--border-soft); |
| } |
| .result-table thead th { |
| background: var(--accent); color: white; |
| font-weight: 600; font-size: var(--fs-3); |
| } |
| .result-table tbody tr.group-row td { |
| background: var(--bg-emphasis); font-weight: 700; |
| text-align: left; |
| color: var(--accent-deep); |
| padding-left: calc(8 * var(--u)); |
| border-bottom: calc(2 * var(--u)) solid var(--accent); |
| } |
| .result-table tbody tr.ours td { background: var(--gold-soft); font-weight: 700; } |
| .result-table tbody tr.ours td:first-child { color: var(--accent-deep); } |
| .result-table .method { text-align: left; padding-left: calc(8 * var(--u)); } |
| .result-table .best { color: var(--accent); font-weight: 700; } |
| |
| .result-table .ref, .result-table .ref td { color: var(--text-muted); } |
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| .keybox { |
| display: grid; |
| grid-template-columns: repeat(3, 1fr); |
| gap: calc(4 * var(--u)); |
| margin: calc(4 * var(--u)) 0 0; |
| } |
| .keybox .kb-item { |
| background: var(--bg-emphasis); |
| border-top: calc(2 * var(--u)) solid var(--accent); |
| padding: calc(3 * var(--u)); |
| text-align: center; |
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| display: flex; flex-direction: column; justify-content: center; |
| border-radius: 0 0 calc(3 * var(--u)) calc(3 * var(--u)); |
| } |
| .kb-item .kb-num { |
| font-family: var(--font-sans); |
| font-weight: 800; |
| font-size: var(--fs-7); |
| color: var(--accent); |
| line-height: 1; |
| } |
| .kb-item .kb-label { |
| font-family: var(--font-sans); |
| font-size: var(--fs-2); |
| color: var(--text-secondary); |
| margin-top: calc(2 * var(--u)); |
| line-height: 1.15; |
| } |
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| .footer { |
| grid-column: 1 / -1; |
| display: flex; justify-content: space-between; align-items: baseline; |
| |
| |
| flex-wrap: wrap; gap: calc(2 * var(--u)) calc(10 * var(--u)); |
| padding-top: calc(8 * var(--u)); |
| border-top: calc(1 * var(--u)) solid var(--border-soft); |
| font-family: var(--font-sans); |
| font-size: var(--fs-4); |
| color: var(--text-muted); |
| } |
| |
| .footer .repo { color: var(--accent); font-weight: 600; overflow-wrap: anywhere; } |
| |
| .footer .method-name { color: var(--accent-deep); } |
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| .ornament { |
| position: absolute; |
| right: calc(18 * var(--u)); |
| bottom: calc(18 * var(--u)); |
| font-family: var(--font-sans); |
| font-size: var(--fs-9); |
| color: var(--ornament-ink); |
| font-weight: 900; |
| letter-spacing: 4px; |
| pointer-events: none; |
| user-select: none; |
| } |
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| .equation-stack { margin: calc(2 * var(--u)) 0; } |
| .equation-stack .eqn { margin: calc(2 * var(--u)) 0; padding: calc(2 * var(--u)) calc(8 * var(--u)); } |
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| .eqn-anatomy { |
| display: grid; grid-template-columns: 1fr 1fr; |
| gap: calc(2.5 * var(--u)); margin: calc(3 * var(--u)) 0; |
| } |
| .eqn-anatomy.eqn-anatomy--row { grid-template-columns: repeat(4, 1fr); } |
| .eqn-anatomy .ea-item { |
| background: var(--bg-card-tint); |
| border: 1px solid var(--border-soft); |
| border-left: calc(2 * var(--u)) solid var(--accent); |
| border-radius: calc(2 * var(--u)); |
| padding: calc(2 * var(--u)) calc(4 * var(--u)); |
| font-size: var(--fs-2); line-height: 1.3; |
| } |
| .eqn-anatomy .ea-tag { |
| display: inline-block; font-family: var(--font-sans); font-weight: 700; |
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| <body> |
| <div class="poster" data-measure-role="poster"> |
| <header class="header" data-measure-role="header"> |
| <div class="venue-badge"><div class="vb-venue">ICML</div><div class="vb-year">2026</div><div class="vb-tag">REPRODUCTION</div></div> |
| <div class="title-block"> |
| <h1 class="title"><span class="accent">Exactly Computing</span> do-Shapley Values</h1> |
| <div class="subtitle">Direct pseudocode implementation, 108 exact graph audits, 600 ADMGs, and boundary sampling</div> |
| <div class="authors-line"><span class="author">Independent reproduction</span> · <span class="aff">Paper #18355 · OpenReview Peim0KY6ty · seed 18355</span></div> |
| </div> |
| <div class="right-block"><div class="qr-label">6 claims<br>CPU only</div></div> |
| </header> |
|
|
| <div class="body-grid" data-measure-role="body"> |
| <div class="column" data-measure-role="column"> |
| <div class="card highlight" data-measure-role="card" data-logbook-target="claim-1-do-shapley-values-can-be-computed-exactly-in-o-r-d-e-t-time-where" data-logbook-label="Claim 1 evidence"> |
| <div class="section-title"><span class="num">1</span><span class="st-text">Compress the coalition powerset</span></div> |
| <p class="body-text">Interventions with the same basis and closure have one value. The Shapley sum can therefore be regrouped <span class="nowrap">without approximation.</span></p> |
| <div class="eqn"><span class="label">Class decomposition</span>$$\phi_i=\sum_{c=1}^{r}\nu(c)w_i(c)$$</div> |
| <table class="result-table"><thead><tr><th class="method">Exact graph audits</th><th>$d$</th><th>Max error</th></tr></thead><tbody> |
| <tr><td class="method">108</td><td>5–10</td><td class="best">$5.88\times10^{-15}$</td></tr> |
| </tbody></table> |
| <p class="body-text mt-3">Every compressed answer was checked against the literal $2^d$ Shapley definition using independent random class values.</p> |
| </div> |
|
|
| <div class="card" data-measure-role="card" data-logbook-target="claim-2-theorem-5-1-shows-the-do-shapley-value-phi-i-is-identifiable-if" data-logbook-label="Claim 2 evidence"> |
| <div class="section-title"><span class="num">2</span><span class="st-text">Singletons diagnose identifiability</span></div> |
| <p class="body-text">The appendix's Boolean ID recursion was implemented on directed and bidirected observed graphs.</p> |
| <div class="eqn"><span class="label">Theorem 5.1 consequence</span>$$\bigwedge_{j=1}^{d}\operatorname{ID}(\{j\},Y)\iff\bigwedge_{S\subseteq[d]}\operatorname{ID}(S,Y)$$</div> |
| <table class="result-table"><thead><tr><th class="method">Random ADMGs</th><th>Coalition queries</th><th>Violations</th></tr></thead><tbody> |
| <tr><td class="method">600</td><td>8,204</td><td class="best">0</td></tr> |
| </tbody></table> |
| <p class="body-text mt-3">406 graphs contained a non-identifiable coalition; the bow-arc negative control was also correctly rejected.</p> |
| </div> |
|
|
| <div class="card" data-measure-role="card" data-logbook-target="claim-3-lemma-3-1-establishes-that-for-any-closed-set-with-a" data-logbook-label="Claim 3 evidence"> |
| <div class="section-title"><span class="num">3</span><span class="st-text">Closed-set removal powers traversal</span></div> |
| <p class="body-text">For every closed set generated in all 108 graph instances, each basis element was removed and closure was <span class="nowrap">recomputed from scratch.</span></p> |
| <table class="result-table"><thead><tr><th class="method">Audit</th><th>Failures</th></tr></thead><tbody> |
| <tr><td class="method">Lemma removal property</td><td class="best">0</td></tr> |
| <tr><td class="method">Algorithm 2 vs brute classes</td><td class="best">0</td></tr> |
| <tr><td class="method">Duplicate class representatives</td><td class="best">0</td></tr> |
| </tbody></table> |
| <div class="callout"><strong>Consequence:</strong> the traversal visits one closure per class instead of scanning the powerset.</div> |
| <p class="body-text mt-3">The independent comparator enumerated every coalition, canonicalized its basis–closure pair, and required exact set equality with Algorithm 2's output.</p> |
| </div> |
|
|
| <div class="card highlight" data-measure-role="card" data-logbook-target="claim-4-algorithm-3-boundary-sampling-guarantees-discovery-of-min-m-r-distinct" data-logbook-label="Claim 4 evidence"> |
| <div class="section-title"><span class="num">4</span><span class="st-text">Every boundary query is distinct</span></div> |
| <p class="body-text">Algorithm 3 was tested at budgets below, at, and above $r$ on 90 independent DAGs.</p> |
| <div class="eqn"><span class="label">Observed invariant</span>$$|\mathcal C|=\min(m,r)$$</div> |
| <table class="result-table"><thead><tr><th class="method">Budget checks</th><th>Duplicate / count failures</th></tr></thead><tbody> |
| <tr><td class="method">450</td><td class="best">0</td></tr> |
| </tbody></table> |
| <p class="body-text mt-3">Each graph was checked at five budgets spanning one query, fractional lattice coverage, exactly $r$, and beyond $r$.</p> |
| <div class="callout"><strong>Queue exhaustion:</strong> at $m\geq r$, traversal returned each class exactly once.</div> |
| </div> |
| </div> |
|
|
| <div class="column" data-measure-role="column"> |
| <div class="card highlight" data-measure-role="card" data-logbook-target="claim-5-the-doregressionmsr-estimator-consistently-outperforms-baseline-variants-and-exhibits-a" data-logbook-label="Claim 5 evidence"> |
| <div class="section-title"><span class="num">5</span><span class="st-text">Structural phase transition <span class="tag-key fs-3 nowrap">★ HEADLINE</span></span></div> |
| <p class="body-text text-secondary fs-3 mb-1">Reduced diagnostic on a $d=9$, $r=133$ graph; 160 trials per budget ratio.</p> |
| <div class="figure"><img class="w-75" src="results/summary.png" alt="Exact class computation, boundary discovery, reduced phase transition, and sparse graph scaling"><div class="caption fs-2"><strong>Bottom:</strong> boundary sampling spends every query on a new class and switches to the exact sum at $m=r$.</div></div> |
| <div class="callout callout--tight"><strong>At $m=r$:</strong> boundary exact in 160/160 trials; random coalitions exact in 0/160.</div> |
| </div> |
|
|
| <div class="card" data-measure-role="card" data-logbook-target="claim-5-the-doregressionmsr-estimator-consistently-outperforms-baseline-variants-and-exhibits-a" data-logbook-label="Claim 5 scope"> |
| <div class="section-title"><span class="num">5a</span><span class="st-text">What was—and was not—rerun</span></div> |
| <table class="result-table"><thead><tr><th class="method">$m/r$</th><th>Boundary error</th><th>Random diagnostic</th></tr></thead><tbody> |
| <tr><td class="method">0.25</td><td class="best">0.0947</td><td>0.3989</td></tr> |
| <tr><td class="method">0.50</td><td class="best">0.0304</td><td>0.3968</td></tr> |
| <tr><td class="method">0.75</td><td class="best">0.00725</td><td>0.3364</td></tr> |
| <tr><td class="method">1.00</td><td class="best">0</td><td>0.3404</td></tr> |
| </tbody></table> |
| <p class="body-text mt-2 fs-3">These are partial class sums, not doRegressionMSR. The named estimator superiority remains Figure-5/source evidence because no author <span class="nowrap">implementation was linked.</span></p> |
| </div> |
|
|
| <div class="card" data-measure-role="card" data-logbook-target="claim-6-real-world-causal-structures-tend-to-be-sparse-so-the-number" data-logbook-label="Claim 6 evidence"> |
| <div class="section-title"><span class="num">6</span><span class="st-text">Sparse graphs compress dramatically</span></div> |
| <table class="result-table"><thead><tr><th class="method">$d=12$ family</th><th>Median $r$</th><th>$r/2^d$</th></tr></thead><tbody> |
| <tr><td class="method">Chain</td><td>13</td><td class="best">0.0032</td></tr> |
| <tr><td class="method">Sparse shortcuts</td><td>155.5</td><td>0.0380</td></tr> |
| <tr><td class="method">Moderate shortcuts</td><td>946.5</td><td>0.2311</td></tr> |
| </tbody></table> |
| <p class="body-text mt-2 fs-3">This independently verifies the sparsity mechanism over 648 synthetic DAGs. It does not replace the paper's 156 learned TALENT structures, whose machine-readable graphs were not released.</p> |
| <p class="body-text fs-2">Synthetic audit supports the mechanism; the real-data trend remains source-only.</p> |
| </div> |
|
|
| <div class="card" data-measure-role="card" data-logbook-target="conclusion" data-logbook-label="Scope and rerun"> |
| <div class="section-title"><span class="num">7</span><span class="st-text">Reproducibility boundary</span></div> |
| <ul><li><strong>Exact:</strong> Claims 1, 3, and 4 directly rerun from pseudocode.</li><li><strong>ID consequence:</strong> Claim 2 checked <span class="nowrap">over 600 ADMGs.</span></li><li><strong>Partial:</strong> Claim 5 named estimator and Claim 6 real-data endpoint remain source-only.</li><li><strong>Compute:</strong> 16.6 s, CPU only, seed 18355.</li></ul> |
| <div class="callout gold mt-3"><strong>Bottom line:</strong> four claims directly supported; two empirical claims honestly bounded.</div> |
| </div> |
| </div> |
| </div> |
|
|
| <div class="footer" data-measure-role="footer"><div><strong class="method-name">DO-SHAPLEY</strong> · ICML 2026 · exact algorithm audit</div><div>OpenReview: <span class="repo">Peim0KY6ty</span> · arXiv: <span class="repo">2602.07203</span></div></div> |
| <div class="ornament">CLASSES · LATTICES · REPRODUCTION</div> |
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