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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-n3ixv3rg.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n3ixv3rg.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n3ixv3rg.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n3ixv3rg.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n3ixv3rg.nxe :where(ul,ol){padding-left:20px}#nxe-n3ixv3rg.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n3ixv3rg.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n3ixv3rg.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n3ixv3rg.nxe svg{display:none}#nxe-n3ixv3rg.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n3ixv3rg.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n3ixv3rg.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-scene nx-es-scene\" data-scene=\"text\" data-ground=\"none\" data-es-org=\"gcri\" aria-labelledby=\"nx-scene-title\"><div class=\"nx-scene-intro nx-es-intro\"><p class=\"nx-eyebrow\"><abbr title=\"The Global Centre for Risk and Innovation\">GCRI<\/abbr> \u00b7 Labs<\/p><span class=\"nx-scene-rule\" aria-hidden=\"true\"><\/span><h1 id=\"nx-scene-title\" class=\"nx-scene-title\">Causal inference for pilots<\/h1><p class=\"nx-scene-lede\">Causal inference separates what an intervention does from what merely accompanies it. In <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/labs\" data-tc=\"Labs\" data-tk=\"Nexus Standards\" data-td=\"The research pillar of the programme, one of six (OR-11, OR-12)\">labs<\/a> and pilots it shapes the design: randomisation where possible, credible comparisons where not, and explicit assumptions in both. It is how a pilot becomes evidence that an innovation works, for whom and under what conditions.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/nexus-labs\/write-to-us\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/nexus-labs\/stem\/\">Back to STEM foundation of Labs<\/a><\/div><\/div><\/section><section id=\"nx-s-1-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-1\"><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\">Contents<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Causal inference for pilots in five parts.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"protocol\"><div class=\"nx-hero-panel\"><div class=\"nx-lv-steps\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Protocol<\/strong><\/span><span class=\"nx-eyebrow\">5 steps<\/span><\/div><ol><li><a href=\"#nx-sec-application\"><span class=\"nx-lv-steps-n\">01<\/span><span class=\"nx-lv-steps-t\">Application<\/span><span class=\"nx-lv-steps-d\">What causal inference and impact evaluation does here<\/span><\/a><\/li><li><a href=\"#nx-sec-data\"><span class=\"nx-lv-steps-n\">02<\/span><span class=\"nx-lv-steps-t\">Data<\/span><span class=\"nx-lv-steps-d\">What the method consumes<\/span><\/a><\/li><li><a href=\"#nx-sec-checks\"><span class=\"nx-lv-steps-n\">03<\/span><span class=\"nx-lv-steps-t\">Validation<\/span><span class=\"nx-lv-steps-d\">How results are checked<\/span><\/a><\/li><li><a href=\"#nx-sec-connections\"><span class=\"nx-lv-steps-n\">04<\/span><span class=\"nx-lv-steps-t\">Connections<\/span><span class=\"nx-lv-steps-d\">Topics that rely on this method<\/span><\/a><\/li><li><a href=\"#nx-sec-stewardship\"><span class=\"nx-lv-steps-n\">05<\/span><span class=\"nx-lv-steps-t\">Stewardship<\/span><span class=\"nx-lv-steps-d\">Who maintains the method<\/span><\/a><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-2\"><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\">Application<\/span><\/div><h2 id=\"nx-s-2\" 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 research and evidence, methods and modelling and innovation, causal inference shows which <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 risk-reduction\" data-tk=\"Nexus risk vocabulary\" data-td=\"Preventing new risk, reducing existing risk and managing residual risk, all contributing to resilience.\" data-ts=\"NRS-C1\" data-tr=\"draft\">risk reduction<\/a> 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\">Randomised pilots and stepped rollouts<\/h3><p class=\"nx-p nx-mt-1\">Where an innovation reaches sites in sequence, the order is randomised so early and late sites form a comparison, with the design and analysis registered beforehand.<\/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=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Quasi-experimental programme evaluation<\/h3><p class=\"nx-p nx-mt-1\">Difference-in-differences, regression discontinuity or synthetic control designs estimate effects where randomisation was not possible, with placebo tests to probe their assumptions.<\/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\"><circle cx=\"12\" cy=\"12\" r=\"10\"\/><polygon points=\"16.24 7.76 14.12 14.12 7.76 16.24 9.88 9.88 16.24 7.76\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Transporting effects from pilot to scale<\/h3><p class=\"nx-p nx-mt-1\">Causal diagrams state which features of the pilot site an effect depends on, so planners can judge whether a new site shares them before expecting the same result.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-s-3-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-3\"><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\">Monitor<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Impact evaluation.<\/h2><p class=\"nx-lede\">The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-labs-method-causal\" data-cx=\"labs.method-causal\" data-variant=\"section\" aria-label=\"Impact evaluation: illustrative console with example 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\">Labs \u00b7 Causal inference for pilots \u00b7 Run R-7061<\/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&#8217;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 Labs<\/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-labs-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||0:39.4;1:38;2:40.4;3:44.8;4:50.2;5:54.8;6:55.6;7:52.8;8:50.1;9:50.2;10:54;11:49.8;12:44.9;13:48.8;14:53.6;15:59;16:56.5;17:53;18:48.1;19:47.8;20:42.7;21:48;22:50.9;23:52.4~Placebo|acc|l||0:47.9;1:50.3;2:50.9;3:48.7;4:47.2;5:43.8;6:45.2;7:47.8;8:49.9;9:48.6;10:50.6;11:53.8;12:57;13:53.9;14:51.3;15:48.4;16:49.2;17:47.3;18:47.4;19:50.8;20:51.1;21:51.2;22:50.3;23:49.2\" 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 evaluation<\/dd><\/div><div><dt>Design<\/dt><dd>difference in differences<\/dd><\/div><div><dt>Units<\/dt><dd>196<\/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-labs-method-causal-t2\"><table class=\"nx-fk-table\" data-fk-table=\"cx-labs-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-labs-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-labs-method-causal:r2-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Drop early adopters<\/td><td data-num=\"true\" data-sv=\"0.26\">0.26<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><tr data-fk-key=\"cx-labs-method-causal:r2-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Synthetic control<\/td><td data-num=\"true\" data-sv=\"0.12\">0.12<\/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&#8217;s structure, terms and states for Labs.<\/span><span data-honesty=\"true\">Example names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-4\"><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\">Data<\/span><\/div><h2 id=\"nx-s-4\" 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\/nexus-labs\/research\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><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\">Trial and pilot outcome data<\/td><td data-label=\"Role\">Effect estimation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Administrative records<\/td><td data-label=\"Role\">Outcomes and covariates for comparison groups<\/td><td data-label=\"Label\"><span class=\"nx-chip\">Published statistics<\/span><\/td><\/tr><tr><td data-label=\"Data\">Registered pre-analysis plans<\/td><td data-label=\"Role\">Fixing estimands and tests<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Site characteristic surveys<\/td><td data-label=\"Role\">Heterogeneity and transportability<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gcri\/G08\/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\/G08\/stem\/causal-inference\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-audience\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-5\"><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\">Methods and evidence<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">How causal inference for pilots is modelled, measured and tested.<\/h2><p class=\"nx-lede\">The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-au\" data-au-voice=\"science\" data-au-n=\"1\"><figure class=\"nx-au-c\" data-au-fig=\"method-panel\"><h3 class=\"nx-h4\">Model, equations and assumptions<\/h3><p class=\"nx-p nx-au-what\">The model structure used for causal inference for pilots, the assumptions it makes and how each is tested.<\/p><div class=\"nx-au-eqs\"><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Risk of element i<\/span><code>R<sub>i<\/sub> = f(H<sub>i<\/sub>, E<sub>i<\/sub>, V<sub>i<\/sub>, C<sub>i<\/sub>)<\/code><span class=\"nx-au-n\">hazard, exposure, vulnerability and coping capacity<\/span><\/div><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Expected annual loss<\/span><code>EAL = &#8721;<sub>k<\/sub> p<sub>k<\/sub> &#183; L<sub>k<\/sub><\/code><span class=\"nx-au-n\">over events k with annual probability p<sub>k<\/sub><\/span><\/div><div class=\"nx-au-eq\"><span class=\"nx-au-k\">Coupled systems<\/span><code>x<sub>t+1<\/sub> = A x<sub>t<\/sub> + B u<sub>t<\/sub> + &#949;<sub>t<\/sub><\/code><span class=\"nx-au-n\">A carries the couplings, u the shock, &#949; the error<\/span><\/div><\/div><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Assumption<\/th><th scope=\"col\">Why it is made<\/th><th scope=\"col\">How it is tested<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Assumption\">Hazard and exposure are independent within an event<\/td><td data-label=\"Why it is made\">Keeps the loss integral tractable<\/td><td data-label=\"How it is tested\">Compare with a copula fit on joint records<\/td><\/tr><tr><td data-label=\"Assumption\">Couplings are linear near the operating point<\/td><td data-label=\"Why it is made\">Lets shocks propagate through A<\/td><td data-label=\"How it is tested\">Perturb beyond the linear range and compare<\/td><\/tr><tr><td data-label=\"Assumption\">Vulnerability curves transfer between places<\/td><td data-label=\"Why it is made\">Local curves are rarely available<\/td><td data-label=\"How it is tested\">Validate against observed losses where they exist<\/td><\/tr><\/tbody><\/table><\/div><dl class=\"nx-au-kv\"><div><dt>Inputs<\/dt><dd>versioned datasets with provenance<\/dd><\/div><div><dt>Outputs<\/dt><dd>distributions, never single numbers<\/dd><\/div><div><dt>Code<\/dt><dd>released with the model card and tests<\/dd><\/div><div><dt>Reproduction<\/dt><dd>same data, code, environment and seed<\/dd><\/div><\/dl><figcaption class=\"nx-caption\"><span>Standard risk and state-space formulations; the assumptions listed are typical of the method, stated for review, not results.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-chips nx-au-fw\" aria-label=\"Frameworks cited\"><span class=\"nx-chip\">FAIR guiding principles \u00b7 GO FAIR and FORCE11<\/span><span class=\"nx-chip\">W3C PROV \u00b7 World Wide Web Consortium<\/span><span class=\"nx-chip\">Guidance on consistent treatment of uncertainties \u00b7 IPCC<\/span><span class=\"nx-chip\">CEOS Analysis Ready Data \u00b7 Committee on Earth Observation Satellites<\/span><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-6\"><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\">Validation<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Identification assumptions are probed through placebo tests, falsification checks and <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 sensitivity-analysis\" data-tk=\"Nexus risk vocabulary\" data-td=\"Testing how much a model's outputs change when inputs or assumptions change, to identify which drive results.\" data-ts=\"NRS-C1\" data-tr=\"draft\">sensitivity analysis<\/a>. 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\">Balance and pre-trend checks<\/td><td data-label=\"How it is done\">Comparison groups are tested for similarity before treatment<\/td><\/tr><tr><td data-label=\"Check\">Placebo and falsification tests<\/td><td data-label=\"How it is done\">Effects are sought where none should exist<\/td><\/tr><tr><td data-label=\"Check\">Sensitivity to unobserved confounding<\/td><td data-label=\"How it is done\">Analyses report how strong a hidden confounder would need to be to change the conclusion<\/td><\/tr><tr><td data-label=\"Check\">Replication across sites<\/td><td data-label=\"How it is done\">Effects are re-estimated in independent settings 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-s-7-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-7\"><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\">Process<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">How the process runs.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-pe\" id=\"nx-pe-prisma\" data-nx-pe=\"nx-pe-prisma\" data-active=\"prisma\"><div class=\"nx-pe-shell\"><p class=\"nx-pe-eb\">Process<\/p><h2 class=\"nx-pe-title\">Causal inference for pilots: PRISMA 2020 review reporting<\/h2><p class=\"nx-pe-cap\">PRISMA 2020 is a reporting guideline for systematic reviews; the steps follow its checklist, not a mandated sequence, and no Nexus document adopts it.<\/p><ol class=\"nx-pe-steps\"><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">01 Plan<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">01<\/span><span class=\"nx-pe-st\">Registration and protocol<\/span><span class=\"nx-pe-k\">Record \u00b7 The review team<\/span><span class=\"nx-pe-sm\">The guideline asks for registration details, access to the protocol and any amendments.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">02<\/span><span class=\"nx-pe-st\">Eligibility criteria<\/span><span class=\"nx-pe-k\">Record \u00b7 The review team<\/span><span class=\"nx-pe-sm\">The guideline asks for the inclusion and exclusion criteria and how studies were grouped.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">02 Find and select<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">03<\/span><span class=\"nx-pe-st\">Search: sources and strategies<\/span><span class=\"nx-pe-k\">Step \u00b7 The review team<\/span><span class=\"nx-pe-sm\">The guideline asks for the information sources and the full search strategies. It does not fix which sources are used.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">04<\/span><span class=\"nx-pe-st\">Selection of studies<\/span><span class=\"nx-pe-k\">Step \u00b7 The review team<\/span><span class=\"nx-pe-sm\">The guideline asks how studies were judged eligible, including how many reviewers screened. It does not fix the number of reviewers.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">05<\/span><span class=\"nx-pe-st\">Flow diagram<\/span><span class=\"nx-pe-k\">Record<\/span><span class=\"nx-pe-sm\">The flow diagram shows the number of records found, the number of studies included and the reasons for exclusion.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">03 Analyse<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">06<\/span><span class=\"nx-pe-st\">Data collection<\/span><span class=\"nx-pe-k\">Step \u00b7 The review team<\/span><span class=\"nx-pe-sm\">The guideline asks how data were collected from reports and which data items were sought.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">07<\/span><span class=\"nx-pe-st\">Risk of bias<\/span><span class=\"nx-pe-k\">Step \u00b7 The review team<\/span><span class=\"nx-pe-sm\">The guideline asks for the methods and tools used to assess risk of bias.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">08<\/span><span class=\"nx-pe-st\">Synthesis<\/span><span class=\"nx-pe-k\">Model \u00b7 The review team<\/span><span class=\"nx-pe-sm\">The guideline asks for the effect measures and the synthesis methods. It does not prescribe meta-analysis.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">09<\/span><span class=\"nx-pe-st\">Certainty of evidence<\/span><span class=\"nx-pe-k\">Step \u00b7 The review team<\/span><span class=\"nx-pe-sm\">The guideline asks for the methods used to assess certainty, and reports the result per outcome. It does not name a grading scale.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">04 Report<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">10<\/span><span class=\"nx-pe-st\">Review report<\/span><span class=\"nx-pe-k\">Record<\/span><span class=\"nx-pe-sm\">The guideline helps authors report why the review was done, what they did and what they found.<\/span><\/li><\/ol><\/li><\/ol><p class=\"nx-pe-foot\"><span><a class=\"nx-link\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/reports\" rel=\"noopener\">Reports<\/a><\/span><span>Outline of an externally published reporting guideline, not a Nexus process and not a record of operation. PRISMA 2020 mainly covers reviews of intervention effects. No Nexus page names it. Every programme is at service level R0.<\/span><\/p><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-8\"><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\">Connections<\/span><\/div><h2 id=\"nx-s-8\" 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\/nexus-labs\/topics\/research-protocols\/\"><span>Research protocols<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/field-and-lab-testbeds\/\"><span>Field and lab testbeds<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/\"><span>AI in scientific workflows<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-work-lab\/\"><span>Future of work lab<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-media-lab\/\"><span>Future of media lab<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-energy-and-water-labs\/\"><span>Future of energy and water labs<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-health-and-bio-labs\/\"><span>Future of health and biotechnology labs<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/reproducibility-and-negative-results\/\"><span>Reproducibility and negative results<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><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=\"project\" 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\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-9\" 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-10-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-10\"><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\">Connected<\/span><\/div><h2 id=\"nx-s-10\" 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\/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\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-11-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-11\"><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\">Questions<\/span><\/div><h2 id=\"nx-s-11\" 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. Effects found in one context may not transfer to another.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Identification assumptions are probed through placebo tests, falsification checks and sensitivity analysis. Replication across settings tests external validity.<\/div><\/details><details><summary>Are the values on this page real?<\/summary><div class=\"nx-answer\">The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method&#8217;s use, not a run; no result is reported here.<\/div><\/details><details><summary>Where else is this method applied?<\/summary><div class=\"nx-answer\">The <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/causal-inference\/\">canonical method page<\/a> lists every platform that applies it.<\/div><\/details><\/div><\/div><\/div><\/section><section id=\"nx-sec-nr-gateway\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-nr-gateway-h\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">Reports<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contribute<\/span><\/div><h2 id=\"nx-nr-gateway-h\" class=\"nx-section-title\">Contribute evidence from this page.<\/h2><p class=\"nx-lede\">Reports is the Consortium&#8217;s quarterly publication. 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