Dear Stata Community!

I hope you can help me out with some of the questions I have regarding the nearest neighbour test in Stata. It's my first time using this program for doing research so please excuse me for what might seem like basic questions about Stata. I have tried my best to do proper research before asking these questions, however, some of the possible solutions are not clear to me still.

Goal
I want to use the nearest neighbour matching to compare fraudulent crowdfunding campaigns with "normal" campaigns on IndieGoGo on several outcome variables. (e.g Funds Raised Percent (funds_raised_percent) but also Number of FB friends).

So far I have collected 60 fraudulent (treated) campaigns and I have circa 19,000 "normal" campaigns. I specifically want to do nearest neighbour matching so I can find the most comparable control campaigns and further collect data on those as well in order to compare the treated sample on those further data points. I would like to do this with replacement in order to decrease the potential for biases.

Problem
I think using the teffects nnmatch command in Stata is the best way to go. When running this in Stata
Code:
gen obs=_n
sort obs 
teffects nnmatch (funds_raised_percent goal2 yr category_n currency_n) (fraud), nn(1) gen(match)
I do get some results and the new variables match1 till match14 that shows which observations are matched with each other. Nevertheless, I want to exactly match on year (yr), category (category_n) and currency (currency_n) but when I put in the following code.
Code:
teffects nnmatch (funds_raised_percent goal2) (fraud), ematch(yr category_n currency_n)
I get the error no exact matches for observation 57; use option osample() to identify all observations with deficient matches.
When adding this to the code. I again get the error 16585 observations have no exact matches; they are identified in the osample() variable.

Only when making the osample 3 times it would run it but this made my limited treatment sample drop by half.
Code:
teffects nnmatch (funds_raised_percent goal2) (fraud), ematch(yr category_n currency_n) osample(no_match)

teffects nnmatch (funds_raised_percent goal2) (fraud) if no_match == 0, ematch(yr category_n currency_n) osample(no_match2)

teffects nnmatch (funds_raised_percent goal2) (fraud) if no_match == 0 & no_match2 == 0, ematch(yr category_n currency_n) osample(no_match3)

teffects nnmatch (funds_raised_percent goal2) (fraud) if no_match == 0 & no_match2 == 0 & no_match3 == 0, ematch(yr category_n currency_n) osample(no_match4)
This was one of the solutions given when doing my research on possible answers regarding this question. As you can see unfortunately it does not work in my case. A potential problem could be that indeed the matching categories are too restrictive, however, with such a large dataset on control variables I have at least 900 observation for each category. Same goes for currency. Therefore, I do not think that by combining categories will solve this problem. Are there any other solutions you guys suggest?

Additional Questions
Furthermore, I have a question about the command pscore in Stata. I see a lot of methods first indeed calculating the pscore before doing the psmatch2 command. However, when I look for examples using teffects nnmatch or teffects psmatch I don't see them doing this. Is this only a necessity when using psmatch2?

Additionally, as I said I want to compare the two groups on multiple outcomes as I said before. Do I simply run the command each time but then for every different outcome or is there a better way to do this?

Lastly, I want to make sure that my data is robust. I found examples where they use the command pscore and then graphically check or use pstest to evaluate the matches Stata has given them. Is this also possible when using teffects? Or are there any other ways to make sure that the results are robust by doing different robustness checks.

Here you can find an extract of the first 100 observations of my Data. I hope that so far everything about my questions is clear and I would be very grateful if anyone wants to discuss these matters with me.

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float fraud double funds_raised_percent float(goal2 close_date2) long(category_n currency_n)
1            14.4182     50000 20375 22 11
1              .3644     10000 20429  9 11
1             .15964     50000 20539 17 11
1            72.5397     10000 20076 17 11
1 1.7944170771756978      9135 19711 17 11
1            5.96116     50000 20287 17 11
1           15.02533    100000 19582  9 11
1 10.743513281919451     29175 21436 12 11
1            197.105      4000 20828 22 11
1            615.295      5000 21534 19 11
1            61.4848     10000 21277 12 11
1           30.69185     20000 21023  2 11
1  45.92786666666667     30000 20801 19 11
1 11.887853333333334     75000 20568 19 11
1             .46478     50000 21384  9 11
1  80.68761333333333     75000 20181 22 11
1             7.6123     30000 20560  2 11
1            12.0938      5000 21413 12 11
1            61.0788     20000 21252 11 11
1            65.3859     20000 21307 11 11
1            75.0848     20000 21344 19 11
1           45.16225     20000 21428  3 11
1           197.8408     10000 21387  9 11
1           300.3877     10000 21161 13 11
1             6.9476      5000 21346 17 11
1            17.4329     30000 21380 12 11
1 16.357866666666666     30000 21162 12 11
1           16.57308     25000 21399 22 11
1            2.46165     80000 20943 13 11
1  4.814493333333333     75000 21181 12  5
1            69.9352     20000 21393 22 11
1          26.513475     80000 20470  2 11
1          41.270525     40000 20463 12 11
1              4.789     25000 21042  3 11
1            3.81288     25000 20700  8  6
1            265.455      3000 21193  2 11
1  103.1740649404028      4866 21103  9 11
1             3.4396     10000 21219 19 11
1            138.031      1000 19922 22 11
1          2.3247125     80000 20934 22 11
1            5.80118     50000 21428 22 11
1 3.6510333333333334     30000 21289 22 11
1             .08903    100000 20752 22 11
1            28.0302     50000 19992  9 11
1           10.80585    100000 19828 12 11
1            .406605   1000000 20274 10 11
1 53.102716636922466     15129 21314 12 11
1          3.1738125    160000 20386 13 11
1  6.181226626776365     13370 20095 17 11
1           7.449975     40000 19703 17 11
1            204.342       500 19804 17 11
1            3.86392    100000 19692 12 11
1            8.81946     50000 20680 12 11
1              5.137      2000 21295 17 11
1            28.2963     10000 21074 17 11
1           238.7733     10000 21575 17 11
0               .518       500 21066  9  6
0               .007    100000 20964  5 11
0              .0025     12000 19499  8 11
0              .0436     50000 21267 25  2
0      .024333333333      3000 19772  4 11
0               .065     30000 21730 10 11
0               .289     10000 21037 14 11
0     2.481066666667 15935.484 21166  9 11
0               .528      5000 19073 12 11
0      .000416666667     36000 20335 19 11
0               .216      2500 19998 14 11
0      .539090669246     11371 21864 26 11
0             .11755     20000 19935 23 11
0              1.064     20000 21165 24 11
0                1.2       750 20202 18 11
0              .0735     10000 20319 15 11
0      .021666666667      6000 20111 18  5
0      .199333333333     15000 19762 14  2
0               .194      3000 20612  4  5
0     2.934666666667  1544.298 20930 11 11
0               .005     30000 20927 13 11
0               .149     10000 19601  1  5
0      .016834189378     40691 21378 24 11
0               .024      5000 21697 11 11
0            .013432    250000 20121  8 11
0             1.0172   15476.8 21882 15 11
0                1.2       500 20553 23 11
0                .39       500 21740  5  6
0      .125566666667    120000 20321 22 11
0      .007857142857     21000 21367  5  2
0      .171833333333      6000 19084  8 11
0            1.03612  62175.23 20918  1 11
0               .524     20000 21637 21 11
0              1.135      1000 20022 26  2
0                  1      6000 20317 17 11
0             5.3128  5423.129 21739 22 11
0             1.0198  10133.36 21777  1 11
0              2.451      1000 21723 26 11
0      .399230769231     13000 21644 16 11
0     1.101230769231      6500 21430 20 11
0            31.6692  2545.565 21636 22  6
0      .003706666667     75000 20120 18 11
0    11.283083399105  4397.025 21856 13 11
0            15.7015  13103.59 21642 17 11
end
format %tdMon_DD,_CCYY close_date2
label values category_n category_n
label def category_n 1 "Art", modify
label def category_n 2 "Audio", modify
label def category_n 3 "Camera Gear", modify
label def category_n 4 "Comics", modify
label def category_n 5 "Culture", modify
label def category_n 8 "Environment", modify
label def category_n 9 "Fashion & Wearables", modify
label def category_n 10 "Film", modify
label def category_n 11 "Food & Beverages", modify
label def category_n 12 "Health & Fitness", modify
label def category_n 13 "Home", modify
label def category_n 14 "Human Rights", modify
label def category_n 15 "Local Businesses", modify
label def category_n 16 "Music", modify
label def category_n 17 "Phones & Accessories", modify
label def category_n 18 "Photography", modify
label def category_n 19 "Productivity", modify
label def category_n 20 "Tabletop Games", modify
label def category_n 21 "Transportation", modify
label def category_n 22 "Travel & Outdoors", modify
label def category_n 23 "Video Games", modify
label def category_n 24 "Web Series & TV Shows", modify
label def category_n 25 "Wellness", modify
label def category_n 26 "Writing & Publishing", modify
label values currency_n currency_n
label def currency_n 2 "CAD", modify
label def currency_n 5 "EUR", modify
label def currency_n 6 "GBP", modify
label def currency_n 11 "USD", modify