분류 전체보기
-
code example (6): matching - nearest neighbor covariateCausality/3 2025. 3. 18. 08:13
ttps://mixtape.scunning.com/05-matching_and_subclassification#exact-matchingSubclassification uses the difference between treatment and control group units and achieves covariate balance by using the K probability weights to weight the averages. The subclassification method is using the raw data, but weighting it so as to achieve balance. We are weighting the differences, and then summing over t..
-
code example (5): subclassificationCausality/3 2025. 3. 17. 22:32
https://mixtape.scunning.com/05-matching_and_subclassificationInsofar as there exists a conditioning strategy that will satisfy the backdoor criterion, then you can use that strategy to identify some causal effect. We now discuss three different kinds of conditioning strategies. They are subclassification, exact matching, and approximate matching. Subclassification is a method of satisfying the ..
-
code example (4): Randomization InferenceCausality/3 2025. 3. 17. 11:32
https://mixtape.scunning.com/04-potential_outcomes#methodology-of-fishers-sharp-nullFisher's sharp null & Steps to a p-valueYou can never let the fundamental problem of causal inference get away from you: we never know a causal effect. We only estimate it. And then we rely on other procedures to give us reasons to believe the number we calculated is probably a causal effect. Randomization infere..
-
code example (3): Independence assumptionCausality/3 2025. 3. 17. 09:12
https://mixtape.scunning.com/04-potential_outcomes#physical-randomization Simple difference in means decompositionIt’s biased because the individuals units were optimally sorting into their best treatment option, creating fundamental differences between treatment and control group that are a direct function of the potential outcomes themselves. Independence assumptionIf treatment is randomly ass..
-
code example (2): collider biasCausality/3 2025. 3. 16. 22:11
https://mixtape.scunning.com/03-directed_acyclical_graphs Notice that the two variables are independent, random draws from the standard normal distribution, creating an oblong data cloud. But because “movie star” is in the top 85th percentile of the distribution of a linear combination of talent and beauty, the sample consists of people whose combined score is in the top right portion of the joi..
-
code example (1): collider biasCausality/3 2025. 3. 16. 21:42
https://mixtape.scunning.com/03-directed_acyclical_graphsThink of a DAG as like a graphical representation of a chain of causal effects. The causal effects are themselves based on some underlying, unobserved structured process, one an economist might call the equilibrium values of a system of behavioral equations, which are themselves nothing more than a model of the world. All of this is captur..
-
..Campus Life 2025. 3. 14. 18:46
1교시 수업은 힘들어요 T.T출근시간이 겹치면 힘들어지기 때문에 5시에 기상해서 부지런히 나와야한다.덕분에 학교 도착은 7시 쯤이 된다. 강의실에서 홀로 공부하는 것도 나쁘진 않지만이른 아침 기상은 힘겨워. 의외로 수업 시간에는 졸립지가 않다. 말똥말똥하다. 오히려 vision은 오후 수업인데 난감할 정도로 잠이 온다.눈 부릅떠야 함. ;;;나 진짜 수업 열심히 듣고 싶은데 왜 잠이 오냐고. 점심을 먹고 들어가서 그런가봐. 이제 vision 수업 전에 밥 안먹으려고.굶고 들어갈 거임.오늘 무척 행복했는데복도를 지나가다가 그 분 목소리를 들었다.목소리만 들어도 가슴 떨리네.목소리만 들어도 너무 좋아. 아.. 이거 너무 심각한데.. 내가 이 정도로 심각하게 누굴 좋아한 적이 있나..?아무리 생각해도 떠오르지..
-
우리 일단..Campus Life 2025. 3. 13. 00:32
부탁이 있는데..장난이 아니고, 진지한 부탁입니다.그래서 진지하게 보이려고 글씨체도 진지한 글씨체로 바꿨습니다. 정말 이상한 소리처럼 들리겠지만, 우리 먼저 아기부터 만들까요? 이상한 소리로 들릴 거 알지만 ㅜㅜ서로 알아가고, 친해지고, 그러다가 감정이 생기고, 그러다가 사귀고, 연애하다가 결혼하고, 그 다음에 아기 갖으려면.. 어느..세월에.. 그 정도의 시간적 여유가 없어요 ㅜㅜ 여자는 생물학적으로 가임 연령이 어느정도 정해져있어요..그리고 요새 워낙 난임이 많아서시도(?)한다고 성공한다는 보장이 없어요.. 그러니까 우리 일단 먼저 아기를 만들고, 그 다음에 알아가고 친해지는 거 어때요?순서를 조금 바꾸는 거죠. 하하 ;;; 이렇게 말하면 남자들 다 도망가겠네어느 누가 이해해주겠니.. 옛날 으른들 ..