This simple answer is, you can't know.  When you implement a change and observe a result, it can be due to the change, be a false change like due to the Hawthorne Effect, your bias meaning you see a change but there is none, or be a random result.  The only way to know, to arrive at a deduced conclusion that stands up to scrutiny, is to conduct many observations in a controlled environment and with some statistical analysis to rule out randomness and experimenters' bias.  And when I say many, I mean many.  Not like two or three sprints.  Remember, you can flip a fair coin ten times and get eight heads.  That does not change the 50/50 likelihood to 80/20.  Observations can be trusted after hundreds of them, not just a few.

The take-a-way of knowing about Hawthorne, bias, and stochastic observations is about knowing how to apply a healthy skeptism of what you are observing.  In other words, stick to the null hypothesis until proven otherwise.  If you make a change and you observe some favorable results, the finding you walk away with is, 'this seemed to cause that,' not 'this caused that.'