{"id":161,"date":"2026-10-13T12:05:19","date_gmt":"2026-10-13T12:05:19","guid":{"rendered":"https:\/\/therisk.global\/health-nexus\/?page_id=161"},"modified":"2026-10-13T19:06:28","modified_gmt":"2026-10-13T19:06:28","slug":"causal-inference","status":"publish","type":"page","link":"https:\/\/therisk.global\/health-nexus\/stem\/causal-inference\/","title":{"rendered":"Causal inference for public health"},"content":{"rendered":"\n<!-- Nexus network page \u00b7 https:\/\/therisk.global\/health-nexus\/stem\/causal-inference\/ \u00b7 source template \u00b7 generated by integrations\/wordpress\/network\/build.mjs, never edit this file --><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-fig-engine\" 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auto;padding:0 16px;font-family:ui-sans-serif,system-ui,-apple-system,\"Segoe UI\",sans-serif;line-height:1.55;overflow-wrap:break-word}#nxe-n9kvzxqj.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n9kvzxqj.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n9kvzxqj.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n9kvzxqj.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n9kvzxqj.nxe :where(ul,ol){padding-left:20px}#nxe-n9kvzxqj.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n9kvzxqj.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n9kvzxqj.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n9kvzxqj.nxe svg{display:none}#nxe-n9kvzxqj.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n9kvzxqj.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n9kvzxqj.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"workbench\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>G04 \u00b7 METHOD \u00b7 causal-inference<\/strong>GCRI dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GCRI<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Health Nexus<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Established<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Method page \u00b7 Causal inference and impact evaluation<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Causal inference for public health<\/h1><p class=\"nx-tagline\">Causal inference asks whether an intervention or exposure actually changed health outcomes, compared with what would otherwise have happened. In this field it evaluates public health measures, service investments and environmental exposures where randomisation is often impossible, and states the assumptions each estimate rests on.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/health-nexus\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/health-nexus\/stem\/\">Back to STEM foundation of Health Nexus<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Method record<\/strong><\/span><span class=\"nx-eyebrow\">causal-inference<\/span><\/div><ol class=\"nx-register-rows\"><li><div><span class=\"nx-register-k\">Method<\/span><span class=\"nx-register-v\">Causal inference and impact evaluation<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Steward<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/economic-development\/\">Economic Development<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Applied in<\/span><span class=\"nx-register-v\">Health Nexus<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Data<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-data\">4 inputs<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Checks<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-checks\">3 validation steps<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Canonical page<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/causal-inference\/\">STEM Commons<\/a><\/span><\/div><\/li><\/ol><p class=\"nx-register-foot\">GCRI \u00b7 Established \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-s-3-sec\"><span>02<\/span>Impact evaluation, in outline<\/a><a href=\"#nx-sec-application\"><span>03<\/span>What causal inference and impact\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-field\"><span>05<\/span>Access to care in the Amazon Basin<\/a><a href=\"#nx-sec-canvas\"><span>06<\/span>The method as a run graph<\/a><a href=\"#nx-sec-data\"><span>07<\/span>What the method consumes<\/a><a href=\"#nx-sec-checks\"><span>08<\/span>How results are checked<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"health\" data-variant=\"stack\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Public health impact evaluation is the use of causal inference to judge whether an intervention or exposure changed health outcomes compared with what would otherwise have happened. It covers public health measures, service investments and environmental exposures where randomisation is often impossible, and states each estimate's assumptions.<\/p><\/div><\/div><\/section><section id=\"nx-s-3-sec\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Console<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Impact evaluation, in outline.<\/h2><p class=\"nx-lede\">The working screen behind this method: its structure, terms and states. Names are placeholders and values illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-health-method-causal\" data-cx=\"health.method-causal\" data-variant=\"section\" aria-label=\"Impact evaluation: illustrative console with placeholder names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Impact evaluation<\/h3><p class=\"nx-fk-sub\">Health \u00b7 Causal inference for public health \u00b7 Run R-9605<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure's values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>Parallel trends hold<\/span><span class=\"nx-cx-prod\">GCRI \u00b7 Health Nexus<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"4\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Event study<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Event study\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-health-method-causal-c0\" data-fk-xs=\"lin,0,23,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-series=\"Treated minus control|ink|l||0:41.9;1:40.7;2:38.6;3:40;4:35.9;5:32.9;6:31.6;7:34.8;8:38.8;9:44.1;10:47.5;11:47.4;12:49.4;13:50;14:54.8;15:56.6;16:51.3;17:46.4;18:49;19:53.2;20:51.4;21:56;22:50.6;23:54~Placebo|acc|l||0:43.2;1:43.4;2:41.4;3:38;4:38.8;5:38.4;6:38.6;7:37.7;8:41.1;9:37.3;10:39.8;11:37.8;12:34.3;13:31.4;14:29.4;15:33.1;16:36.4;17:38.4;18:38;19:40.3;20:37.5;21:34.9;22:31.1;23:30.6\" data-nx-z=\"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\"><\/svg><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"2\" data-t=\"kv\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Method card<\/h4><\/div><\/div><dl class=\"nx-fk-dl\"><div><dt>Method<\/dt><dd>Causal inference and impact\u2026<\/dd><\/div><div><dt>Design<\/dt><dd>difference in differences<\/dd><\/div><div><dt>Units<\/dt><dd>86<\/dd><\/div><div><dt>Label<\/dt><dd><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>MODELLED<\/span><\/dd><\/div><\/dl><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Robustness checks<\/h4><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-health-method-causal-t2\"><table class=\"nx-fk-table\" data-fk-table=\"cx-health-method-causal-t2\" aria-label=\"Robustness checks\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Check<\/button><\/th><th scope=\"col\" data-col=\"c1\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Estimate<\/button><\/th><th scope=\"col\" data-col=\"c2\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Holds<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-health-method-causal:r2-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Main specification<\/td><td data-num=\"true\" data-sv=\"0.06\">0.06<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><tr data-fk-key=\"cx-health-method-causal:r2-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Drop early adopters<\/td><td data-num=\"true\" data-sv=\"0.1\">0.10<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><tr data-fk-key=\"cx-health-method-causal:r2-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Synthetic control<\/td><td data-num=\"true\" data-sv=\"0.11\">0.11<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Impact evaluation: the screen's structure, terms and states for Health.<\/span><span data-honesty=\"true\">Placeholder names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"health\" data-variant=\"split\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">What causal inference and impact evaluation does here.<\/h2><p class=\"nx-lede\">Causal inference and impact evaluation develop methods to determine whether and how interventions or exposures cause outcomes. They separate genuine effects from correlation. In health and biosecurity, causal inference shows which risk reduction measures actually work. It supports allocating resources toward interventions with demonstrated effect across connected risks. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/causal-inference\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><circle cx=\"12\" cy=\"12\" r=\"10\"\/><circle cx=\"12\" cy=\"12\" r=\"6\"\/><circle cx=\"12\" cy=\"12\" r=\"2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Evaluating public health measures<\/h3><p class=\"nx-p nx-mt-1\">Difference-in-differences and synthetic control designs compare places that adopted a measure with comparable places that did not, and trends before adoption are examined before any effect is reported.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M12 2v2\"\/><path d=\"m4.93 4.93 1.41 1.41\"\/><path d=\"M20 12h2\"\/><path d=\"m19.07 4.93-1.41 1.41\"\/><path d=\"M15.947 12.65a4 4 0 0 0-5.925-4.128\"\/><path d=\"M3 20a5 5 0 1 1 8.9-4H13a3 3 0 0 1 2 5.24\"\/><path d=\"M11 20v2\"\/><path d=\"M7 19v2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Exposure-response for heat and air<\/h3><p class=\"nx-p nx-mt-1\">Time-series and case-crossover designs relate daily exposure to outcomes in the same population, controlling for season and trend, to estimate lagged exposure-response functions.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2\"\/><circle cx=\"9\" cy=\"7\" r=\"4\"\/><path d=\"M22 21v-2a4 4 0 0 0-3-3.87\"\/><path d=\"M16 3.13a4 4 0 0 1 0 7.75\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Impact of access investments<\/h3><p class=\"nx-p nx-mt-1\">New facilities, fee removals or outreach programmes are evaluated with stepped rollout, regression discontinuity or matching, so the effect on use and outcomes is separated from background change.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Counterfactual<\/td><td data-label=\"Definition\">What would have happened without the intervention or exposure, which every causal estimate approximates.<\/td><\/tr><tr><td data-label=\"Term\">Interrupted time series<\/td><td data-label=\"Definition\">A design that compares an outcome's trend before and after an intervention.<\/td><\/tr><tr><td data-label=\"Term\">Confounding<\/td><td data-label=\"Definition\">Distortion of an estimated effect by a factor linked to both the exposure and the outcome.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"health\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Geography<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">Access to care in the Amazon Basin.<\/h2><p class=\"nx-lede\">How far each division sits from a large city, with the roads and airports that carry patients and supplies, shown for the Amazon Basin as an example geography for this subject.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-map nx-cmap\" data-nx-map=\"ctx\" data-cmap=\"amazon:health\" data-ramp=\"red\" data-basis=\"case\" data-focus=\"0 0 2000 1400|485 108 1927 1400|146 386 762 1333|222 0 851 601|716 882 1324 1400\"><figure class=\"nx-fk\" data-fk=\"map-amazon-health\" data-ramp=\"red\" data-nx-z=\"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\"><figcaption class=\"nx-fk-cap\"><span>How far each division sits from a large city, with the roads and airports that carry patients and supplies.<\/span><span data-honesty=\"true\">Indicator derived from Natural Earth at map scale; a screening view, not an assessment.<\/span><\/figcaption><\/figure><div class=\"nx-live-slot nx-live-slot--map\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gcri\/G04\/stem\/causal-inference\/#map\" data-nxf-title=\"Health Nexus live map\" data-nxf-height=\"560\" hidden><\/div><div class=\"nx-map-hud\"><span class=\"nx-eyebrow\">GCRI \u00b7 Health Nexus \u00b7 field<\/span><span class=\"nx-eyebrow nx-map-hud-right\">DESIGN FIGURE<\/span><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-canvas\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">The method as a run graph.<\/h2><p class=\"nx-lede\">Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Method canvas<\/strong> \u00b7 Causal inference and impact evaluation as applied here<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-g04-stem-causal-inference-prisma-t nx-dag-g04-stem-causal-inference-prisma-d\" data-nx-z=\"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\"><title id=\"nx-dag-g04-stem-causal-inference-prisma-t\">Causal inference and impact evaluation in Health Nexus: systematic evidence synthesis<\/title><desc id=\"nx-dag-g04-stem-causal-inference-prisma-d\">A question is framed with its owner and a protocol registered; records are searched, screened and checked for eligibility by two reviewers; data are extracted, synthesised and graded for certainty before a brief is written. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 01<\/strong><span>Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"health\" data-variant=\"stack\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Data<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by causal inference and impact evaluation on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/research\/sources\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">Routine health information systems<\/td><td data-label=\"Role\">Outcomes before and after interventions<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Policy and measure trackers<\/td><td data-label=\"Role\">Timing and stringency of interventions<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Daily mortality and admissions<\/td><td data-label=\"Role\">Outcome series for exposure studies<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OFFICIAL<\/span><\/td><\/tr><tr><td data-label=\"Data\">Reanalysis and air quality fields<\/td><td data-label=\"Role\">Exposure series<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gcri\/G04\/stem\/causal-inference\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G04\/stem\/causal-inference\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Validation<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Identification assumptions are probed through placebo tests, falsification checks and sensitivity analysis. Replication across settings tests external validity.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Pre-trend and placebo tests<\/td><td data-label=\"How it is done\">Effects are tested in periods and places where none should exist<\/td><\/tr><tr><td data-label=\"Check\">Sensitivity to unmeasured confounding<\/td><td data-label=\"How it is done\">Bounds or E-values state how strong hidden confounding would need to be to explain the result<\/td><\/tr><tr><td data-label=\"Check\">Replication across settings<\/td><td data-label=\"How it is done\">Effects are compared across countries or periods before they are generalised<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Unobserved confounding and spillovers can bias estimates. Effects found in one context may not transfer to another.<\/p><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section\" data-hue=\"health\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/health-system-continuity\/\"><span>Health system continuity<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/infectious-threats\/\"><span>Infectious threats<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/health-equity\/\"><span>Health equity<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/environmental-health\/\"><span>Environmental health<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/health-nexus\/topics\/digital-health-risk\/\"><span>Digital health risk<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-stewardship\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/economic-development\/\">Economic Development<\/a> stewards causal inference and impact evaluation for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/causal-inference\/\">Canonical page: Causal inference and impact evaluation<\/a><\/p><\/div><\/div><\/section><section id=\"nx-s-12-sec\" class=\"nx-section\" data-hue=\"health\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Where this page leads across the Consortium.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/stem\/mathematics\/causal-inference\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Canonical method page<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Causal inference and impact evaluation<\/span><span class=\"nx-dest-why\">Platform application links to the canonical method page.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/economic-development\/stem\/causal-inference\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Impact evaluation<\/span><span class=\"nx-dest-why\">Economic Development stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-13-sec\" class=\"nx-section\" data-hue=\"health\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of causal inference and impact evaluation in this field?<\/summary><div class=\"nx-answer\">Unobserved confounding and spillovers can bias estimates. 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