You cannot randomize. What is the strongest causal claim you can still make?

hard

Answer

$One conditional on an assumption you state explicitly, which is the honest form of every observational causal claim.$
$Begin by drawing the assumed causal structure, because which variables to adjust for is a question about that structure, not about which improve fit; conditioning on a collider or a mediator introduces bias rather than removing it.$
$If you can argue that all confounders are measured, adjustment or matching gives an effect estimate under that assumption.$
$Stronger designs exploit structure instead: a difference-in-differences comparison if you have pre-period data and a plausible parallel trend, an instrumental variable if something shifts treatment without affecting the outcome directly, or a regression discontinuity if assignment follows a threshold.$
$Then test the assumption, with placebo outcomes and pre-trend checks, and report sensitivity to unmeasured confounding.$

Check yourself — multiple choice

None, only randomization allows causal claims
A claim conditional on a stated identification assumption: draw the causal structure, adjust for confounders not colliders, prefer difference-in-differences, instruments or discontinuities, then run placebo and sensitivity checks
Correlation is enough with big data
Add every available control variable

Observational causality rests on an explicit identification assumption plus falsification and sensitivity analysis.

#causal-inference#quasi-experimental

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