# Why use casual inference over simple year over year analysis? [closed]

Let's say I'm trying to measure the effect of a marketing campaign on dwell time (length of stay at a location).

Why not just use a simple year over year calculation to measure the effect? E.g. 20% increase in time from last year. Assuming other main factors such as weather/seasonality/day of the month affect dwell time, wouldn't this calculation take this into account since it roughly maintains these variables constant?

What drawbacks does this approach have? Why should I use a more complicated method such as modeling with casual inference? In essence, why should I go down the road of time-series analysis instead of just comparing original data from the same period each year?

## closed as unclear what you're asking by Nick Cox, John, usεr11852, mdewey, gung♦Oct 29 '16 at 21:45

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• Well, you can. Moreover, no one approach in statistics is always best, try several and see which works best. – Carl Oct 24 '16 at 22:37
• Casual $\ne$ causal. Affect $\ne$ effect. A more serious problem is that it's far from clear precisely what your alternatives mean. – Nick Cox Oct 28 '16 at 20:39
• Sorry, I made an attempt to change casual to causal but after reading the question I am not sure what it's asking, I reverted my edit. – Penguin_Knight Oct 28 '16 at 20:41