I'm a data scientist at a credit union. I have some DS experience elsewhere, but I'm still wet behind the ears, so to speak. Our data department is small and I'm the only one with DS knowledge. I know explainability to important when working in financial institutions because we get audited. Therefore, I'll be building a lot of logistic regression and decision trees in my future. That said, we are interested in understanding the potential impact of rate changes (fed and our own) on deposits, loan growth, etc as well as understanding our loan portfolio risk. My thought was to use bayesian modeling so we can understand the uncertainty. That said, I wasn't sure if this could cause issues when we're auditing, even though we wouldn't be using the model to impact customer decisions (approve loans, etc.). Does anyone have any advice on this? Thank you!

For a game, I'm simulating opening packs of cards, and my simulation results aren't quite holding up to the math that I've done.

The concept of the game is that you're opening packs of cards until you have a complete set, then you sell the set and can use the money to open more exclusive (and expensive) packs.

In each pack, there are 6 cards from a 'common' list, 3 cards from an 'uncommon' list, and a 50/50 shot at a 'rare' card. If the final card is not rare, it's drawn from the uncommon list.

There are 25 total commons, 10 uncommons, and 5 rares.

My understanding is that based on the stamp collector's problem, the expected number of trials to collect all cards from a set is equal to n Hn, where Hn is the nth harmonic number.

I've chosen to model the expected number of packs to open as MAX (E(T) common / 6, E(T) uncommon / 3.5, E(T) rare * 2).

The rationale is that there are six commons, so you'd naturally divide by E(T) common by 6 because you actually have 6 attempts in each card pack.

The reason I have chosen to take the maximum value of these three is that because you're drawing from all three lists at the same time, the expected number of packs should be equal to the hardest one of the three to complete.

The issue I have is that after running experiments (1M simulated packs), the average number of packs to get a full set is about 10% higher than the E(T) I've calculated.

Would somebody mind helping me learn where I've gone wrong?

Calculations:

E(T) common = 25/6 * H25 = 15.90

E(T) uncommon = 10/3.5 * H10 = 8.80

E(T) rare = 5*2 * H5 = 22.83

Overall E(T) should be 22.83

Experimental results:

avg: 24.702346837944663, max: 119, min: 8, med: 22, mode: 18 (2286 occurrences)

Hi everyone,

I have a treatment (i.e. a treatment dose), say X, that can take on values from 0–5 and is continuous in between, i.e. bounded continuous. A value of 0 will also be relatively common.

I’m specifying a treatment model for the conditional density given confounders L, i.e f(X|L) and an outcome model for Y~X to estimate the marginal E(Y^x).

My question is how to best treat the treatment variable 1) when it’s the outcome/dependent variable and 2) when it’s the exposure/independent variable in such scenario.

I don’t think I should be using model fit to pick a conditional distribution, and for interpretation sake I think assuming X has either a linear or quadratic relationship with Y is simplest. For the treatment model specifying a normal conditional density as long as the covariate balance is reasonable would be easiest I guess too.

Maybe someone has a better idea that doesn’t make interpretation too difficult, since it’s for a medical paper that people without strong statistical backgrounds will also read.