3 ms·
They're "data scientists", what did you expect.
by thanatropism 8y ago
They're "data scientists", what did you expect.
- mlthoughts2018 8y agoI expect they are paid 2x more than trained statisticians unfortunately. Happily though, they didn’t try to solve this problem with Judea Pearl-style causal inference hype. A better approach to this would probably be treating the data as panel data and either doing monthwise hierarchical Bayesian regressions, and testing if the coefficients pooled over time are significantly non-zero, or else using a sort of distributed lag model or instrumental variables model on the monthwise simple regressions. In these cases, it’s better to roll up lagged features of the exogenous time series to serve as constructed covariates in the lag model, than to rely on differencing and time series tests which are like a landmine of interpretation issues. Checking causality in models like this is very tricky, so a lot of care has to be taken, and you should expect work to proceed slowly and require a lot of posterior checks.
- l____ 8y agoHey, thank you for informative comments. I would gladly read more about hierarchical Bayesian regression. Would you recommend any sources from which I could learn more? (I am fully aware that I can google it but in my opinion it's usually better to ask someone who is already familiar with the topic because he/she may know good learning materials.)
- mlthoughts2018 8y ago- < http://www.stat.columbia.edu/~gelman/book/ http://www.stat.columbia.edu/~gelman/book/ > - < http://www.stat.columbia.edu/~gelman/arm/ http://www.stat.columbia.edu/~gelman/arm/ > If you happen to use Python, pymc3 is a great place to look for hierarchical and Bayesian timeseries models with full examples.
- l____ 8y agoThank you. One of my personal goals with regard to this analysis was to learn Python on-the-go (I feel more comfortable in R) so I will definitely check pymc3.