You are trying to determine an appropriate distance measure, and clearly you are noticing how tricky this can be.
Ordinal data is not interval data. You should consider:
- whether the distance between each category is the same (is the distance between 2 and 3 the same as the distance between 3 and 4?)
- whether special consideration needs to be given to the neutral/null category (in your case, 1: no effect).
- whether several of your causes variables need to be considered together. For example, you may decide to sum 2 related variables together and treat them as one (derivative) variable for purposes of clustering.
These considerations have psychological roots. For example, people tend to give more weight to the difference between options at the ends of the scale than in the middle.
You may decide on an approach based on these considerations that involves:
- cleaning the data
- transforming the current scale into one in Euclidean space.
... or, you may decide this is not necessary for your purposes.
Existing research that uses the Likert scale often utilizes distance measures based on Cosine distance and Pearson Correlation.
You may find the following useful: