A LinkedIn series on the framing decisions that determine whether analysis is causal.
Parallel trends is not a technical footnote. It is the assumption that makes the comparison believable. When it breaks, the estimate can still look precise while pointing strategy in the wrong direction.
Not every business problem needs an experiment, but every impact claim needs the right identification strategy. Randomized experiment, DiD, ITS, and synthetic control each carry assumptions the data has to earn.
The hardest part is rarely running the model. It is knowing what kind of answer the data can actually support. That is a judgment problem, not a tooling problem.
Teams measure observed lift but rarely define the right baseline. Without a counterfactual, you are interpreting change, not measuring impact.
Teams scale the wrong strategy when they treat correlations as causal mechanisms. Signal, symptom, and lever are different.
Leadership in data work is making counterfactual assumptions explicit, especially when you can't run an experiment.
Teams confuse the three and end up making confident decisions on the wrong analytical foundation.