Time, Interrupted: Measuring Intervention Effects with Interrupted Time-Series Analysis - Ben Cohen

PyData LA 2018
How can we estimate the impact of a historical event where there is no way to run a controlled experiment? For example, we may want to assess the impact of a TV campaign or account for lost sales during an outage. This talk presents a brief overview of interrupted time series analysis, a technique commonly used in econometrics and public health that is designed to address this type of problem.
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00:00 Welcome!
00:46 Introduction
02:41 What is Interrupted Time Series Analysis
03:53 A/B Testing
04:53 How to measure the impact of a national TV campaign
05.44 Geo-targeting
06:25 How can we know if something we did had an effect
08:05 Counterfactuals
09:23 Interrupted Time Series
13:01 Building a time series counterfactual
14:01 Non-stationarity
15:30 Auto-correlation
16:15 Independent and identically distributed assumptions
17:52 What should the model include
19:45 Prediction intervals
22:19 Prophet library
23:26 Training and prediction
24:53 Assess accuracy of the model
26:20 Compare predictions to observations
26:53 Lift analysis
27:00 Samples from the posterior predictive distribution
27:31 Pointwise vs cumulative estimates
29:38 Answering probability-based questions
30:05 Threats to validity
30:53 Change in the underlying process
32:49 Confounding variables
33:47 Model misspecification
36:27 Q&A
36:35 Business applications
38:26 Situations where it worked or didn't
39:45 Comparing different channels of advertisement
40:50 Data preparation for Interrupted Time Series
41:55 Ramp-up period before measuring the effect
43:06 Assessing whether the counterfactual is correct
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[@amym7075]

really clear presentation! he's such a good communicator

[@ramadhanbagindo1030]

It is like R Package causal impact by google

[@akashchandrayan7255]

Isnt this just an application of forecasting?