Quote:
Originally Posted by ubercapper
An example might be to point out first time starters i turf route races where the sire or dam has had one or more first time out winners in similar races.
This angle absolutely has a negative ROI, but there are nuggets to be found among all the horses which match the angle.
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From 'Causal Inference and Discovery in Python' Aleksander Molak:
"Imagine you work at a research institute and you’re trying to
understand the causes of people drowning.Your organization provides you with a huge database of socioeconomic variables. You decide to
run a regression model over a large set of these variables to predict the number of drownings per day in your area of interest. When you check the results, it turns out that the
biggest coefficient you obtained is for
daily regional ice cream sales. Interesting! Ice cream usually contains large amounts of sugar, so maybe sugar affects people’s attention or physical performance while they are in the water.
This hypothesis might make sense, but before we move forward, let’s ask some questions. How about other variables that we did not include in the model? Did we add enough predictors to the model to describe all relevant aspects of the problem? What if we added too many of them? Could adding just one variable to the model completely change the outcome?"
