Correlation vs causation describes the difference between two variables moving together (correlation) and one variable actually driving a change in another (causation). "Attribution Theatre" is the practice of presenting correlated marketing data, like dashboards showing rising conversions alongside a campaign, as if it proves that campaign caused the growth, when no controlled test has confirmed the link. For SaaS teams, mistaking one for the other pours budget into channels that only look effective.
Correlation means two metrics rise or fall together. Causation means one of those metrics is directly responsible for the change in the other. A SaaS team might notice that trial signups increased the same week they launched a LinkedIn ad campaign. That's a correlation. Whether the ads actually caused those signups, rather than a product launch, a press mention, or seasonal demand doing the work, is a separate question that a correlation alone cannot answer.
Proving causation requires isolating one variable while holding everything else constant, usually through a controlled experiment. Proving correlation requires nothing more than two lines on a chart moving in the same direction. That gap between "moved together" and "caused the movement" is exactly where most attribution reporting breaks down, and it's where Attribution Theatre lives.
Attribution Theatre is the practice of building polished, confident-looking attribution reports that assign credit to marketing channels based on correlation, then presenting that credit as if it were causally proven. It looks like rigor. It behaves like guesswork.
The mechanics are consistent across most B2B SaaS teams that fall into it. A dashboard attributes revenue to a channel, based on last-touch or multi-touch rules. Nobody has run a holdout test to check whether that revenue would have shown up anyway. The team presents the report in a board meeting as fact. Leadership reallocates budget based on that fact. Nobody circles back to check whether the reallocation actually moved the outcome, because the next quarter's dashboard is already due.
The name matters because it captures something reporting jargon usually hides: the performance of certainty, not the substance of it. A large-scale field experiment at eBay is the clearest documented case of this gap. Because search clicks and purchase behavior are correlated, returns from paid search are a fraction of conventional non-experimental estimates, and brand-keyword ads have no measurable short-term benefits (NBER). The study found that if eBay were to shut off its branded search advertisements, traffic to the eBay website would remain virtually unchanged, still flowing through organic search results that cost eBay nothing (CEPR). The attribution dashboards had been running theatre for years, crediting ad spend for sales that would have happened regardless.
SaaS growth teams are structurally exposed to Attribution Theatre because vendors design attribution tools to show correlation, then label them as if they show causation. Google Analytics, most CRM reporting, and default multi-touch models all operate on the same principle: track what happened before a conversion and assign credit accordingly. None of them, by design, can tell you what would have happened without the touchpoint.
Research replicating this pattern beyond eBay found the same substitution effect at smaller but still significant scale. A replication at Edmunds.com found the same substitution effect but at a far smaller magnitude, with less than half of paid search traffic recovered through organic search. The direction of the bias is consistent even when the magnitude varies, which is the pattern any growth team should expect from correlation-based reporting: it tends to overstate impact, not understate it.
The financial consequence compounds quickly for a SaaS company with a lean budget. One analysis of production-scale incrementality work found that last-touch attribution remains the dominant decision signal in digital advertising despite extensive evidence that attributed outcomes can overstate causal impact by 2 to 10 times. A founder reallocating spend based on that overstated number isn't optimising. They're funding the illusion.
For early-stage and growth-stage SaaS teams, this matters more than for large enterprises with dedicated data science functions. A ten-person startup usually has one dashboard, one growth lead, and no capacity to run a parallel causal analysis before every budget decision. Attribution Theatre thrives in exactly that environment: high pressure to show a number, low capacity to question it.
A few consistent signals separate a genuine causal claim from a theatrical one:
Any team that can't answer at least three of these questions about a reported result is running Attribution Theatre, whether they call it that or not.
The fix isn't more dashboards. It's controlled testing. Randomised holdouts, geo-split experiments, and phased rollouts are the standard tools for isolating one variable and observing its real effect, and they don't require a data science team to run at small scale. A SaaS company testing a new onboarding email can hold out 10% of new signups from receiving it and compare activation rates. That single test produces more causal certainty than a year of correlation-based dashboards.
This is where Seedling's approach to growth reporting differs from most default analytics setups: it pushes teams toward testable hypotheses and holdout comparisons rather than static attribution snapshots, so a claimed win has to survive a check before it becomes a budget decision. The goal isn't to distrust every dashboard. It's to build a habit of asking what a metric would look like without the intervention before crediting the intervention.
Attribution Theatre isn't a sign of bad marketing. It's a sign of a reporting culture that rewards confident answers over correct ones. The SaaS teams that grow efficiently aren't the ones with the most sophisticated attribution models. They're the ones willing to test a claim before they act on it, and to admit when the dashboard was showing a coincidence rather than a cause.
Some common questions, answered
Correlation means two variables move together, while causation means one variable directly drives a change in another. For example, trial signups rising during an ad campaign shows correlation, but it does not prove the ads caused the increase.
Attribution Theatre presents correlated marketing data as proof that a channel caused revenue or conversions. This can lead SaaS teams to reallocate limited budgets towards channels that receive credit for outcomes that would have happened anyway.
Teams can use randomised holdouts, geo-split experiments or phased rollouts to compare results with and without an intervention. For example, withholding an onboarding email from 10% of new signups allows the team to compare activation rates and estimate its causal effect.