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우연이라고 하기엔우연일 확률이 너무 희박하다. 그런데 그 희박한 확률을 뚫고너와 내가 만났으니 우리의 만남은 우연이 아니구나! 이거슨"significant하다!!"Under the null hypothesis of no effect, the observed difference could well be due to chance (this highlights the role of statistical significance - whether the observed difference is large enough to conclude that it is unlikely to be due to chance).

https://matheusfacure.github.io/python-causality-handbook/16-Regression-Discontinuity-Design.htmlWe don’t stop to think about it much, but it is impressive how smooth nature is. You can’t grow a tree without first getting a bud, you can’t teleport from one place to another, a wound takes its time to heal. Even in the social realm, smoothness seems to be the norm. You can’t grow a business in one..

https://matheusfacure.github.io/python-causality-handbook/15-Synthetic-Control.htmlOne Amazing Math Trick to Learn What can't be KnownWhen we looked at difference-in-difference, we had data on multiple customers from 2 different cities: Porto Alegre and Florianopolis. The data span 2 different time periods: before and after a marketing intervention was done in Porto Alegre to boost customer depo..

오늘도 9시간 자야겠다. 나는 나의 예상을 벗어나질 않는구나.이렇게 맨날 공부하다가 아기 아빠는 찾을 길이 없겠지.  그래. 이번 생은 그냥 이렇게 자유롭게 살자스라.

synthetic control이 되겠다. 연도가 쌓이면 panel data analysis도 가능할 듯

https://matheusfacure.github.io/python-causality-handbook/14-Panel-Data-and-Fixed-Effects.htmlIn the previous chapter, we explored a very simple Diff-in-Diff setup, where we had a treated and a control group (the city of POA and FLN, respectively) and only two periods, a pre-intervention and a post-intervention period. But what would happen if we had more periods? Or more groups? Turns out this ..

https://matheusfacure.github.io/python-causality-handbook/13-Difference-in-Differences.htmlThree Billboards in the South of BrazilThe problem with billboard and TV ads is that it is hard to know how effective they are. Sure, you could measure the purchase volume, or whatever you want to drive, before and after placing a billboard somewhere. If there is an increase, there is some evidence that th..

https://matheusfacure.github.io/python-causality-handbook/12-Doubly-Robust-Estimation.htmlDon't Put All your Eggs in One BasketWe’ve learned how to use linear regression and propensity score weighting to estimate E[Y|T=1]−E[Y|T=0]|X. But which one should we use and when? When in doubt, just use both! Doubly Robust Estimation is a way of combining propensity score and linear regression in a way y..