{"id":22309,"date":"2026-06-26T11:40:45","date_gmt":"2026-06-26T11:40:45","guid":{"rendered":"https:\/\/lifesight.io\/?page_id=22309"},"modified":"2026-09-09T07:36:43","modified_gmt":"2026-09-09T07:36:43","slug":"research","status":"publish","type":"page","link":"https:\/\/lifesight.io\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

The Science Behind Causal<\/span> Measurement<\/h1><\/div>

Methodology papers, whitepapers and benchmarks on how marketing is actually measured – published in full, reviewed by an external panel of experts, and open to scrutiny.<\/p>\n<\/div><\/div><\/div>

Browse the Library<\/span><\/a><\/div>
Meet the Expert Council<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>

Featured Papers<\/span><\/h2><\/div>

Our Latest Methodology Papers<\/h2><\/div><\/div><\/div>
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\"ensemble<\/span><\/div>

Ensemble Forecasting: A Better Default for Business Forecasting<\/h3><\/div>

Most businesses forecast hundreds of things at once: products, regions, markets, channels. The model that works best for one is rarely the best for the next, and it changes again next quarter. We tested this on 99 real businesses. Picking the best model line by line got it right only one time in four. Running several models together and taking the middle number beat it, and matched the single best model without anyone having to guess which one that was.<\/p>\n<\/div>

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\n\n \"Rajeev\n\n
\n
\n Rajeev Nair\n <\/div>\n
\n Co-founder & CPO, Lifesight\n <\/div>\n <\/div>\n\n<\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
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\"Marketing<\/span><\/div>

The Marketing Context Graph<\/h3><\/div>

A practical framework for causal marketing measurement and auditable budget decisions. Channels influence one another, and budget decisions lose their reasoning the moment they’re made. Marketing Context Graph is a governed, time-aware solution that fixes both, cutting budget-decision error by up to 43% and answering 100% of “why” questions.<\/p>\n<\/div>

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\n\n \"Rajeev\n\n
\n
\n Anil Singh\n <\/div>\n
\n Co-Founder & CTO, Lifesight\n <\/div>\n <\/div>\n\n<\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
<\/span><\/a><\/span>
\"Rise<\/span><\/div>

The Rise of Unified Marketing Measurement<\/h3><\/div>

A four-part guide to why no single method can measure marketing on its own, and how attribution, marketing mix modeling, and experiments combine into one accountable system for deciding where the next dollar goes. This guide outlines the measurement problems most brands face, the UMM framework, how to think like a measurement scientist, and how to enact UMM for yourself.<\/p>\n<\/div>

<\/div><\/div>
\n\n \"Rajeev\n\n
\n
\n Rajeev Nair\n <\/div>\n
\n Co-founder & CPO, Lifesight\n <\/div>\n <\/div>\n\n<\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>

Research Library<\/span><\/h2><\/div>

Papers, Whitepapers & Technical Notes.<\/h2><\/div>

Everything we publish on the science of measurement – from first-principles causal inference to the benchmarks we hold ourselves to.<\/p>\n<\/div><\/div><\/div>

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FORECASTING
\n<\/span><\/h2><\/div>

PLANNING
\n<\/span><\/h2><\/div><\/div><\/div><\/div>

Model Selection and Combination<\/h2><\/div>

Across 99 businesses and 65 organizations, no single forecasting model was reliably the best one. Running several together matched the best available model from day one, without having to know in advance which model that would be.<\/p>\n<\/div>

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Rajeev Nair<\/h3><\/div><\/div><\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
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geo-experiments
\n<\/span><\/p><\/h2><\/div>

marketing-measurement <\/span><\/h2><\/div><\/div><\/div><\/div>

Awesome Marketing Measurement<\/h2><\/div>

A curated, vendor-neutral list of tools, libraries, research, and resources for measuring marketing effectiveness – MMM, incrementality, causal inference, and attribution.<\/p>\n<\/div>

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Rohit Maheswaran<\/h3><\/div><\/div><\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
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MCP
\n<\/span><\/p><\/h2><\/div>

claude-skills
\n<\/span><\/h2><\/div><\/div><\/div><\/div>

Lifesight<\/h2><\/div>

Official agent skills for the Lifesight MCP – causal marketing measurement inside Claude and Claude Code. Claude Code plugin + Claude.ai bundle.<\/p>\n<\/div>

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Rohit Maheswaran<\/h3><\/div><\/div><\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
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Causal Inference
\n<\/span><\/h2><\/div>

Methodology Paper
\n<\/span><\/h2><\/div><\/div><\/div><\/div>

Predictive CLTV Modeling Methodology<\/h2><\/div>

How Lifesight forecasts customer lifetime value using causal ML – accounting for incrementality, channel attribution and long-run retention dynamics to produce audit-grade CLTV estimates.<\/p>\n<\/div>

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Lifesight Research<\/h3><\/div><\/div><\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
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Unified Measurement
\n<\/span><\/h2><\/div>

Methodology Paper
\n<\/span><\/h2><\/div><\/div><\/div><\/div>

UMM Methodology<\/h2><\/div>

The full technical specification of Lifesight’s Unified Marketing Measurement methodology – how MMM, geo-lift experiments and attribution are triangulated into a single, calibrated output.<\/p>\n<\/div>

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Lifesight Research<\/h3><\/div><\/div><\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
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Causal Inference
\n<\/span><\/h2><\/div>

Methodology Paper
\n<\/span><\/h2><\/div><\/div><\/div><\/div>

Causal Reasoning in Modeling<\/h2><\/div>

A technical treatment of how causal graphs, counterfactual estimation and structural assumptions are embedded in Lifesight’s measurement models – giving every recommendation a defensible causal warrant.<\/p>\n<\/div>

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Lifesight Research<\/h3><\/div><\/div><\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
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Causal Inference
\n<\/span><\/h2><\/div>

Technical Note
\n<\/span><\/h2><\/div><\/div><\/div><\/div>

ML-Based Inference<\/h2><\/div>

How Lifesight applies machine learning within a causal framework – using flexible learners for nuisance estimation while preserving the identifiability conditions that make outputs trustworthy.<\/p>\n<\/div>

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Lifesight Research<\/h3><\/div><\/div><\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
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Causal Inference
\n<\/span><\/h2><\/div>

Technical Note
\n<\/span><\/h2><\/div><\/div><\/div><\/div>

Advanced Modeling Scenarios<\/h2><\/div>

Edge cases, complex attribution structures and non-standard measurement challenges – and how Lifesight’s modeling methodology handles them without sacrificing rigor or interpretability.<\/p>\n<\/div>

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Lifesight Research<\/h3><\/div><\/div><\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div>
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MODEL CALIBRATION
\n<\/span><\/h2><\/div>

Technical Note
\n<\/span><\/h2><\/div><\/div><\/div><\/div>

Continuous Model Calibration<\/h2><\/div>

A technical overview of how Lifesight integrates incrementality testing and geo-experiment results to continuously calibrate Marketing Mix Models (MMM), anchoring model outputs to ground-truth causal impact for audit-grade accuracy.<\/p>\n<\/div>

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Lifesight Research<\/h3><\/div><\/div><\/div>
Explore Research<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>

How we do science<\/span><\/h2><\/div>

Measurement You Can Audit, Not Just Trust.<\/h2><\/div>

Most vendors ask you to believe a number. We’d rather you check our work. Four commitments govern everything in this library.<\/p>\n<\/div><\/div><\/div>

01<\/h3><\/div>

Methodological transparency<\/h3><\/div>

We publish our assumptions, our priors, and our failure modes – not just our results. If a method has a known bias, we name it and show how we correct for it.<\/p>\n<\/div><\/div><\/div>

02<\/h3><\/div>

External peer review<\/h3><\/div>

Our methodology papers are reviewed by an independent panel of academics and practitioners who do not work for Lifesight and are free to disagree in public.<\/p>\n<\/div><\/div><\/div>

04<\/h3><\/div>

Vendor-neutral rigor<\/h3><\/div>

The science of measurement is bigger than any one product. We cite the field, credit competing approaches, and write for the discipline first and the demo second.<\/p>\n<\/div><\/div><\/div>

03<\/h3><\/div>

Reproducible benchmarks<\/h3><\/div>

Where we make an accuracy claim, we describe the holdout, the validation procedure, and the conditions under which it holds – so it can be challenged and replicated.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

Expert & Advisory Council<\/span><\/h2><\/div>

The People Who Keep Us Honest<\/h2><\/div>

The Lifesight Research Council is guided by an independent panel of external econometricians, statisticians, and measurement practitioners. This panel serves as an objective guardrail to help us pressure-test our science and methodologies. While each member brings their own area of expertise, they act in an advisory capacity to assist with:<\/p>\n<\/div><\/div><\/div>

<\/span><\/a><\/span>
\n\n
\n \"Ron\n <\/div>\n\n
\n
\n Ron Berman\n <\/div>\n
\n Associate Professor of Marketing, The Wharton School, University of Pennsylvania\n <\/div>\n <\/div>\n\n<\/div><\/div><\/div><\/div>

Causal inference \u00b7 Experimentation \u00b7 Marketing analytics<\/h2><\/div>

Ron Berman researches causal inference, online experimentation and marketing analytics. His work spans attribution, incrementality testing and the statistical foundations of measurement – making him one of the field’s most rigorous voices on whether marketing effect estimates can be trusted.<\/p>\n<\/div><\/div><\/div>

<\/span><\/a><\/span>
\n\n
\n \"Professor\n <\/div>\n\n
\n
\n Kenneth C. Wilbur\n <\/div>\n
\n Professor of Marketing and Analytics, University of California, San Diego\n <\/div>\n <\/div>\n\n<\/div><\/div><\/div><\/div>

ADVERTISING EFFECTIVENESS \u2022 MARKETING ANALYTICS \u2022 DIGITAL MARKETS<\/h2><\/div>

Kenneth Wilbur researches advertising effectiveness, marketing analytics, and digital markets. His work spans customer analytics, media measurement, digital platforms, and the economics of advertising – making him one of the field’s foremost experts on measuring marketing impact and understanding what truly drives performance.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>

Be The First To Hear About
\nOur New Research<\/span><\/h2><\/div>

Sign up for our newsletter and be the first to hear about any new papers we have and get weekly updates on what\u2019s new in the world of marketing measurement.<\/p>\n<\/div>