comparison of output (impact$series$cum.effect) in Python and R packages about tfcausalimpact HOT 2 OPEN

Hi @rj678 ,

The preperiod as given by your assignment would be computed in Python by something like:

preperiod = ci.inferences['post_cum_effects_means'][ci.inferences['post_cum_effects_means'].isna()]

Which essentially retrieves completed predictions of training data. In R package the empty values were assigned as "zeroes" whereas in Python, as they don't exist, remained as NaN.

Notice also that if you want to work with pre_period data it's also available in the ci object in ci.pre_data or ci.normed_pre_data (the latter is same data but with normalization applied).

As for varying results, did the results you observed differ too much from the official README report? I just ran it here and had very close results — using hmc method. They will never be the same as the algorithm behind is not deterministic but they should always converge to the same conclusions and be very close for the most part.

Results are expected to change from the original R package as well but again they should lead to same conclusions and be similar overall. The cumulative field will differ more as it sums up all estimated points in post period.

Let me know if this helps you,

Best,

Will

from tfcausalimpact.

thanks so much for confirming that the empty values are zero in in R, and NaN in Python - from what I remember, the difference between the non-zero values in impact$series$cum.effect and ci.inferences['post_cum_effects_means'] was not insignificant - I'll check again and get back, thanks so much for the detailed response.

from tfcausalimpact.