<b>Beyond A/B tests, what causal tools belong in a marketer's kit?</b>

<b>Beyond A/B tests, what causal tools belong in a marketer's kit?</b>

Most attribution discourse stops at randomized experiments and holdouts. The quasi-experimental toolkit from econometrics is underused in marketing and often fits situations where randomization isn't possible.

<b>Three tools worth knowing</b>

— <i>Difference-in-differences (DiD):</i> compare the before/after change in a treated group against the before/after change in an untreated group. It nets out shared trends, so it's the backbone of most geo-style reads. Its core assumption — <i>parallel trends</i> absent treatment — is testable in the pre-period and must be checked, not assumed.
— <i>Regression discontinuity (RD):</i> when treatment is assigned by a threshold (a loyalty tier at a spend cutoff, a free-shipping minimum), compare units just above and just below the line. They're nearly identical except for treatment, giving near-experimental credibility at the boundary.
— <i>Instrumental variables (IV):</i> when something randomly nudges exposure without directly affecting the outcome (an ad-server delivery hiccup, a platform auction quirk), it can isolate causal effect from confounding. Powerful but fragile — a weak or invalid instrument quietly reintroduces the bias it was meant to remove.

<b>The nuance</b>

These aren't magic. Each rests on assumptions that fail silently: parallel trends, no manipulation around the cutoff, instrument validity. Used carelessly, they produce confident causal claims with no more warrant than the correlation they replaced.

<b>What to actually do</b>

— Reach for DiD when you have a natural treated/control split and a clean pre-period.
— Look for RD anywhere a threshold drives treatment; these moments are causal gold and routinely ignored.
— State and test the identifying assumption out loud before believing the estimate.

Bottom line for practitioners: randomized tests are the gold standard, but quasi-experimental methods extract causation from situations you can't randomize. Their power is entirely contingent on assumptions you must verify, not invoke.