00:11 Introduction to talk
00:31 Package announcement
00:33 Speaker introduction
01:13 Causal inference is trending
01:22 Google Trends on Causal inference
01:51 Is it convincing enough?
02:41 Bayesian model on the trends using PyMC
03:45 Hype Cycle for emerging tech by Gartner
04:10 Difference between Statistical relationships and Causal relationships
06:55 Observational study on causal relationship between Tea and Death
09:00 Confounding variables in our study
09:33 Randomized control trial (RCT)
10:25 Can you model confounding variables and not randomize?
12:17 Randomization is very effective
12:35 Randomized control trials can be problematic
14:56 Quasi-Experimentation by Charles S. Reichardt
16:08 CausalPy package
16:44 What does CausalPy do?
16:50 Example: What was the causal impact of Brexit?
19:00 Normalized GDP
19:55 What do we not have on this graph?
21:18 Fitting the model
22:32 Synthetic control method in CausalPy
23:28 Visualizing the output
26:03 Other features of CausalPy
26:10 Interrupted time series
26:52 Regression discontinuity
27:49 Difference in differences
28:04 Did my advertising budget cause more sales?
29:41 Summary
30:54 Q/A Any suggested resource to properly design RCT?
31:56 Q/A Why didn't you use a diff-diff model?
32:51 Q/A Training a ML model to predict pre-treatment GDP of UK
34:07 Q/A How is CausalPy related to CausalImpact
35:07 Q/A Interrupted time series and regression discontinuity
Video
https://youtu.be/gV6wzTk3o1U
Submitted by @SangamSwadiK
Timestamps