Correct use of Chi-square and hypothesis testing? I have a dataset which contains lots of data on customers and their actions within their journey through our business, one of these actions is how many of our events they have attended, another action is whether they have made a purchase from us. I am testing the hypothesis that attending an event makes someone more likely to make a purchase.
For this problem I used the following hypotheses:
H_0: There is no difference in the percentage of people to make a purchase between the two groups.
H_1: The percentage of people to make a purchase for those that have attended an event is higher than for those that haven't.
To test these hypothesis I produced a contingency table as shown below:





Made a purchase
Didn't make a purchase




Attended an event
190
1350


Didn't attend an event
983
15588




And then used the scipy.stats function 'chi2_contingency()' to do a test of independence. I thought it was right to either use the Chi-Squared test or Fisher's exact test but since the sample size is quite large then Chi-Squared was more suitable:
chistat, pvalue, dof, ex = chi2_contingency(cont_df)
print(chistat, pvalue)

This then gave the output:
(94.39478042474279, 2.5847291047881143e-22)

Which I thought showed that when using significance level of 0.05 there was a statistically signifcant difference between the probability to make a trade between the group that attended an event and the group which didn't, hence rejecting the null hypothesis.
This is my first time applying hypothesis testing to a real world problem and so would appreciate if anyone could point out any flaws in my process/understanding. My main issue with it lies in the fact that people who are going to attend an event are presumably already more likely to make a purchase, so this doesn't necessarily prove any causality between the two variables. As well as this, would it not be better to have a control group which didn't even have the option of attending an event i.e. did receive an email from us advertising the event. Then performing a test using one group as those that were aware of the events, and one of those that weren't and see if there is a statistically significant difference in purchase conversion between those two groups?

All of your concerns are valid and well articulated.  The chi-square test simply provides the weight of the evidence that there is an association between attendance and purchase.  Any causality is an extra layer of interpretation that in many instances is unverifiable.  There are many causal hypotheses one could entertain.
Randomly assigning people to either attend or not attend the event would allow you to make a stronger causal inference argument as to the effect attendance has on making a purchase.  This is because through randomization each subject in the study would be equally likely to attend the event (the distribution of subject features is, in the long run, the same between the two groups).  Any difference in purchasing behavior could then be attributed to attendance alone.
This doesn't mean that all observational studies are useless.  It just means that you have to preface every statement with, "Assuming no unmeasured confounders...[insert causal statement here]," or state that the findings show an association without speculating as to the causal nature.  I generally do both, starting with the latter and finishing with the former.
Addendum: If you have an imbalance in prognostic factors between those who attend and those who do not attend, then for causal inference you have at least a couple of options: 1) adjust the outcome model and interpret the effect of attendance in various subgroups (main effect, interactions).  For causal inference you may consider drawing a causal diagram to understand how the covariates should enter the model.  2) Re-weight the observations using IPTWs.  This balances the measured prognostic covariates between treatment groups to make the sample appear as though it is the result of a randomized trial.  The outcome model can be analyzed using only a main effect for attendance.  Causal inference on the effect of attendance is for the entire population.  With either approach the causal interpretation of the results will still need to be prefaced with, "Assuming no unmeasured confounders..."
