Yes, see this for details on why this is so.
But in probability theory, absence of evidence is always evidence of absence. If E is a binary event and P(H|E) > P(H), "seeing E increases the probability of H"; then P(H|~E) < P(H), "failure to observe E decreases the probability of H". P(H) is a weighted mix of P(H|E) and P(H|~E), and necessarily lies between the two.
Thanks! The second to last paragraph from your EY citation was exactly what I was looking for.
Imagine that you know there is a strong correlation between X and Y. Statistically competent scholars have extensively examined the causal relationship between X and Y and have failed to find a significant causal relationship and have failed to rule out the possibility that there is a significant causal relationship.
Would it be reasonable for you to claim that the causal relationship between X and Y probably isn't too strong or it would have shown up clearly on statistical analysis? At the very least, should learning of the negative results of the scholars cause you to decrease your estimate of the causal relationship between X and Y?